修复人脸识别算法facenet-pytorch实现方式

2026-09-09 19:18:52 +00:00 · 2025-04-01 16:57:01 +08:00
parent b2fc391abc
commit 4eb02c6d87
42 changed files with 238 additions and 129 deletions
--- a/README.md
+++ b/README.md
@@ -61,14 +61,16 @@
 - **RetinaFace 模型**[[GitHub]](https://github.com/deepinsight/insightface/tree/master/detection/retinaface)：一个高效的深度学习人脸检测模型，支持高精度的人脸检测，但目前不支持人脸比对
 - **Ultra-Light-Fast-Generic-Face-Detector-1MB** [[GitHub\]](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB)：一个轻量级的人脸检测模型，适用于需要较低延迟和较小模型尺寸的应用场景。
 - **Seetaface6**  [[GitHub\]](https://github.com/seetafaceengine/SeetaFace6)：是中科视拓最新开放的商业正式级版本，支持人脸检测、关键点定位、人脸识别。同时增加了活体检测、质量评估、年龄性别估计。并且响应时事，开放了口罩检测以及戴口罩的人脸识别模型
+- **[facenet-pytorch](https://github.com/timesler/facenet-pytorch)**  [[GitHub\]](https://github.com/seetafaceengine/SeetaFace6)：这是 pytorch 中 Inception Resnet (V1) 模型的存储库，在 VGGFace2 和 CASIA-Webface 上进行了预训练。Pytorch 模型权重使用从 David Sandberg 的 [tensorflow Facenet repo](https://github.com/davidsandberg/facenet) 移植的参数进行初始化。该存储库中还包含 MTCNN 的高效 pytorch 实现，用于推理之前的人脸检测。这些模型也是经过预训练的。据我们所知，这是最快的 MTCNN 实现。

-### 模型对比及下载地址
+### 人脸模型对比及下载地址

 |         模型名称          |                           下载地址                           | 文件大小 |     适用场景      | 兼容系统            |
 | :-----------------------: | :----------------------------------------------------------: | :------: | :---------------: | ------------------- |
 |        retinaface         | [下载](https://resources.djl.ai/test-models/pytorch/retinaface.zip) |  110MB   |  高精度人脸检测   | Windows/Linux/MacOS |
-| ultralightfastgenericface | [下载](https://resources.djl.ai/test-models/pytorch/ultranet.zip) |  1.7MB   |   高速人脸检测    | Windows/Linux       |
-|        seetaface6         | [下载](https://pan.baidu.com/s/1hfNacA8ISV2qHrycjOkgqA?pwd=1234) |  288MB   | 人脸检测/人脸识别 | Windows/Linux       |
+| ultralightfastgenericface | [下载](https://resources.djl.ai/test-models/pytorch/ultranet.zip) |  1.7MB   |   高速人脸检测    | Windows/Linux/MacOS |
+|        seetaface6         | [下载](https://pan.baidu.com/s/1hfNacA8ISV2qHrycjOkgqA?pwd=1234) |  288MB   | 人脸检测/人脸识别 | Windows             |
+|      facenet-pytorch      | [下载](https://resources.djl.ai/test-models/pytorch/face_feature.zip) |  104MB   |     人脸识别      | Windows/Linux/MacOS |

 ## 环境要求

@@ -82,7 +84,7 @@
 >
 > （1）默认算法（RetinaFace）或轻量算法（Ultra-Light-Fast-Generic-Face-Detector ）都为python算法，兼容 Windows、Linux、MacOS，Android 等系统，SmartJavaAI首次启动将自动下载模型到及依赖库到本地（.djl.ai隐藏文件夹），建议保持网络畅通。初始化完成后，后续启动可实现毫秒级响应。在无网络环境下，可指定本地模型路径（需提前下载模型包）。目前，这两种算法不支持人脸识别或人脸比对功能。
 >
-> （2）Seetaface6 采用 C++ 编写，兼容 Windows、CentOS、Ubuntu 等系统。创建算法时，将自动加载对应系统的依赖库。Seetaface6 支持全功能人脸处理（人脸检测、人脸比对 1:1 或 1:N）。SmartJavaAI 通过 JNI 调用 C++ 接口，不支持在线下载模型，需手动下载并存储至本地。使用人脸比对等功能时，需要将项目中db/faces-data.db存放到您本地路径下并在config中指定人脸库路径。
+> （2）Seetaface6 采用 C++ 编写，兼容 Windows、CentOS、Ubuntu 等系统，虽然Seetaface6 支持linux，但是我们目前仅实现了windows，如果后续对linux需求多，我们将兼容linux。Seetaface6 支持全功能人脸处理（人脸检测、人脸比对 1:1 或 1:N）。SmartJavaAI 通过 JNI 调用 C++ 接口，不支持在线下载模型，需手动下载并存储至本地。使用人脸比对等功能时，需要将项目中db/faces-data.db存放到您本地路径下并在config中指定人脸库路径。

 ### 1. 安装人脸算法依赖

@@ -93,7 +95,7 @@
     <dependency>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-face</artifactId>
-        <version>1.0.5</version>
+        <version>1.0.6</version>
     </dependency>
 </dependencies>
 ```
@@ -150,7 +152,7 @@ log.info("相似度：{}", similar);



-### 6. 人脸特征提取及比对
+### 6. 人脸特征提取及比对（seetaface6）

 ```java
 // 初始化配置
@@ -169,7 +171,24 @@ float similar = currentAlgorithm.calculSimilar(feature1, feature2);
 log.info("相似度：{}", similar);
 ```

-### 7. 注册及搜索人脸（1：N）
+### 7. 人脸特征提取及比对（facenet-pytorch）
+
+```java
+//创建脸算法
+FaceAlgorithm featureAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm();
+//提取身份证人脸特征
+float[] feature1 = featureAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
+float[] feature2 = featureAlgorithm.featureExtraction("src/main/resources/kana2.jpg");
+if (feature1 != null && feature2 != null) {
+    //相似度在0.8至0.85及以上时，可判定为同一人，但具体阈值可能因图片而异，存在一定误差。
+    float similar = featureAlgorithm.calculSimilar(feature1, feature2);
+    log.info("相似度：{}", similar);
+} else {
+    log.warn("人脸特征提取失败");
+}
+```
+
+### 8. 注册及搜索人脸（1：N）

 ```java
 // 初始化配置
@@ -195,7 +214,7 @@ if(faceResult != null){
 }
 ```

-### 8. 人脸检测（离线下载模型）
+### 9. 人脸检测（离线下载模型）

 ```java
 // 初始化配置
@@ -250,7 +269,16 @@ ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString(
 - **微信**: deng775747758 （请备注：SmartJavaAI）
 - **Email**: 775747758@qq.com

-
-
 🚀 **如果这个项目对你有帮助，别忘了点个 Star ⭐！你的支持是我持续优化升级的动力！** ❤️

+
+
+
+## 更新日志
+
+## [v1.0.6] - 2025-04-01
+-  修复人脸识别算法facenet-pytorch实现方式
+-  优化Seetaface6算法，兼容jdk高版本
+
+
+
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -43,7 +43,7 @@
        <dependency>
            <groupId>ink.numberone</groupId>
            <artifactId>smartjavaai-face</artifactId>
-            <version>1.0.5</version>
+            <version>1.0.6</version>
        </dependency>

        <dependency>
--- a/examples/src/main/java/smartai/examples/face/FaceDemo.java
+++ b/examples/src/main/java/smartai/examples/face/FaceDemo.java
@@ -13,8 +13,10 @@ import smartai.examples.utils.ImageUtils;
 import javax.imageio.ImageIO;
 import java.awt.*;
 import java.awt.image.BufferedImage;
+import java.awt.image.RasterFormatException;
 import java.io.File;
 import java.io.FileInputStream;
+import java.io.IOException;
 import java.net.URL;
 import java.nio.file.Files;
 import java.nio.file.LinkOption;
@@ -30,7 +32,7 @@ public class FaceDemo {

    public static void main(String[] args) {
        try {
-            featureComparison();
+            featureExtractionAndCompare2();
            //detectFace2();
            //verifyIDCard();
        } catch (Exception e) {
@@ -44,29 +46,26 @@ public class FaceDemo {
     * 特点：识别精度高，高速
     * 应用场景：如监控摄像头、智能安防系统等需要高精度检测的场合
     */
-    public static void detectFace() throws Exception {
-        // 创建并启动计时器
-        StopWatch sw = StopWatch.createStarted();
-        //创建人脸算法
-        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm();
-        sw.stop();
-        log.info("创建人脸算法耗时：" + sw.getTime() + "ms");
-        sw.reset();
-        sw.start();
-        //使用图片路径检测
-        FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
-        sw.stop();
-        log.info("人脸检测耗时：" + sw.getTime() + "ms");
-        log.info("人脸检测结果：{}", JSONObject.toJSONString(result));
-        //使用图片流检测
-        File input = new File("src/main/resources/largest_selfie.jpg");
-        //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
-        //log.info("人脸检测结果：{}", JSONObject.toJSONString(result));
-        BufferedImage image = ImageIO.read(input);
-        //创建保存路径
-        Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
-        //绘制人脸框
-        ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+    public static void detectFace(){
+        try {
+            //创建人脸算法
+            FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm();
+            //使用图片路径检测
+            FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+            log.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+            //使用图片流检测
+            File input = new File("src/main/resources/largest_selfie.jpg");
+            //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
+            //log.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+            BufferedImage image = ImageIO.read(input);
+            //创建保存路径
+            Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
+            //绘制人脸框
+            ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+
    }


@@ -76,29 +75,25 @@ public class FaceDemo {
     * 特点：高速，准确率略低
     * 应用场景：如监控摄像头、智能安防系统等需要高精度检测的场合
     */
-    public static void detectFace2() throws Exception {
-        // 创建并启动计时器
-        StopWatch sw = StopWatch.createStarted();
-        //创建轻量人脸算法
-        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createLightFaceAlgorithm();
-        sw.stop();
-        log.info("创建人脸算法耗时：" + sw.getTime() + "ms");
-        sw.reset();
-        sw.start();
-        //使用图片路径检测
-        FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
-        sw.stop();
-        log.info("人脸检测耗时：" + sw.getTime() + "ms");
-        log.info("轻量人脸检测结果：{}", JSONObject.toJSONString(result));
-        //使用图片流检测
-        //File imageFile = new File("/Users/wenjie/Downloads/djl-master/examples/src/test/resources/largest_selfie.jpg");
-        //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(imageFile));
-        File input = new File("src/main/resources/largest_selfie.jpg");
-        BufferedImage image = ImageIO.read(input);
-        //创建保存路径
-        Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
-        //绘制人脸框
-        ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+    public static void detectFace2(){
+        try {
+            //创建轻量人脸算法
+            FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createLightFaceAlgorithm();
+            //使用图片路径检测
+            FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+            log.info("轻量人脸检测结果：{}", JSONObject.toJSONString(result));
+            //使用图片流检测
+            //File imageFile = new File("/Users/wenjie/Downloads/djl-master/examples/src/test/resources/largest_selfie.jpg");
+            //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(imageFile));
+            File input = new File("src/main/resources/largest_selfie.jpg");
+            BufferedImage image = ImageIO.read(input);
+            //创建保存路径
+            Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
+            //绘制人脸框
+            ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
    }


@@ -109,34 +104,38 @@ public class FaceDemo {
     * 特点：识别精度高，高速
     * 应用场景：如监控摄像头、智能安防系统等需要高精度检测的场合
     */
-    public static void detectFaceOffine() throws Exception {
-        // 初始化配置
-        ModelConfig config = new ModelConfig();
-        config.setAlgorithmName("retinaface");//人脸算法模型，目前支持：retinaface/ultralightfastgenericface/seetaface6
-        //config.setAlgorithmName("ultralightfastgenericface");//轻量模型
-        config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);//置信度阈值
-        config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);//每张特征图保留的最大候选框数量
-        //nms阈值:控制重叠框的合并程度,取值越低，合并越多重叠框（减少误检但可能漏检）；取值越高，保留更多框（增加检出但可能引入冗余）
-        config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
-        //模型下载地址：
-        //retinaface: https://resources.djl.ai/test-models/pytorch/retinaface.zip
-        //ultralightfastgenericface: https://resources.djl.ai/test-models/pytorch/ultranet.zip
-        //改为模型存放路径
-        config.setModelPath("/Users/wenjie/Documents/develop/face_model/retinaface.pt");
-        //创建人脸算法
-        FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm(config);
-        //使用图片路径检测
-        FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
-        log.info("人脸检测结果：{}", JSONObject.toJSONString(result));
-        //使用图片流检测
-        File input = new File("src/main/resources/largest_selfie.jpg");
-        //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
-        //logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
-        BufferedImage image = ImageIO.read(input);
-        //创建保存路径
-        Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
-        //绘制人脸框
-        ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+    public static void detectFaceOffine(){
+        try {
+            // 初始化配置
+            ModelConfig config = new ModelConfig();
+            config.setAlgorithmName("retinaface");//人脸算法模型，目前支持：retinaface/ultralightfastgenericface/seetaface6
+            //config.setAlgorithmName("ultralightfastgenericface");//轻量模型
+            config.setConfidenceThreshold(FaceConfig.DEFAULT_CONFIDENCE_THRESHOLD);//置信度阈值
+            config.setMaxFaceCount(FaceConfig.MAX_FACE_LIMIT);//每张特征图保留的最大候选框数量
+            //nms阈值:控制重叠框的合并程度,取值越低，合并越多重叠框（减少误检但可能漏检）；取值越高，保留更多框（增加检出但可能引入冗余）
+            config.setNmsThresh(FaceConfig.NMS_THRESHOLD);
+            //模型下载地址：
+            //retinaface: https://resources.djl.ai/test-models/pytorch/retinaface.zip
+            //ultralightfastgenericface: https://resources.djl.ai/test-models/pytorch/ultranet.zip
+            //改为模型存放路径
+            config.setModelPath("/Users/wenjie/Documents/develop/face_model/retinaface.pt");
+            //创建人脸算法
+            FaceAlgorithm currentAlgorithm = FaceAlgorithmFactory.createFaceAlgorithm(config);
+            //使用图片路径检测
+            FaceDetectedResult result = currentAlgorithm.detect("src/main/resources/largest_selfie.jpg");
+            log.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+            //使用图片流检测
+            File input = new File("src/main/resources/largest_selfie.jpg");
+            //FaceDetectedResult result = currentAlgorithm.detect(new FileInputStream(input));
+            //logger.info("人脸检测结果：{}", JSONObject.toJSONString(result));
+            BufferedImage image = ImageIO.read(input);
+            //创建保存路径
+            Path imagePath = Paths.get("output").resolve("retinaface_detected.jpg");
+            //绘制人脸框
+            ImageUtils.drawBoundingBoxes(image, result, imagePath.toAbsolutePath().toString());
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
    }

    /**
@@ -164,7 +163,8 @@ public class FaceDemo {
    }

    /**
-     * 人脸特征提取及比对
+     * seetaface6人脸特征提取及比对（可人证核验）
+     * 目前仅支持windows 64位系统，如需支持其他操作系统可参考方法：featureExtractionAndCompare2
     */
    public static void featureExtractionAndCompare(){
        try {
@@ -180,8 +180,12 @@ public class FaceDemo {
            //提取图像中最大人脸的特征
            float[] feature1 = currentAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
            float[] feature2 = currentAlgorithm.featureExtraction("src/main/resources/kana2.jpg");
-            float similar = currentAlgorithm.calculSimilar(feature1, feature2);
-            log.info("相似度：{}", similar);
+            if(feature1 != null && feature2 != null){
+                float similar = currentAlgorithm.calculSimilar(feature1, feature2);
+                log.info("相似度：{}", similar);
+            }else{
+                log.warn("人脸特征提取失败");
+            }
        }
        catch (Exception e){
            e.printStackTrace();
@@ -189,8 +193,31 @@ public class FaceDemo {
    }

    /**
-     * 注册人脸及搜索人脸（1：N）
+     * facenet-pytorch 人脸特征提取及比对（可人证核验）
+     * 支持windows，linux，macos
     */
+    public static void featureExtractionAndCompare2(){
+        try {
+            //创建脸算法
+            FaceAlgorithm featureAlgorithm = FaceAlgorithmFactory.createFaceFeatureAlgorithm();
+            //提取身份证人脸特征
+            float[] feature1 = featureAlgorithm.featureExtraction("src/main/resources/kana1.jpg");
+            float[] feature2 = featureAlgorithm.featureExtraction("src/main/resources/kana2.jpg");
+            if (feature1 != null && feature2 != null) {
+                //相似度在0.8至0.85及以上时，可判定为同一人，但具体阈值可能因图片而异，存在一定误差。
+                float similar = featureAlgorithm.calculSimilar(feature1, feature2);
+                log.info("相似度：{}", similar);
+            } else {
+                log.warn("人脸特征提取失败");
+            }
+        } catch (Exception e) {
+            e.printStackTrace();
+        }
+    }
+
+        /**
+         * 注册人脸及搜索人脸（1：N）
+         */
    public static void registerAndSearchFace(){
        try {
            // 初始化配置
@@ -248,5 +275,4 @@ public class FaceDemo {
        }
    }

-
 }
--- a/pom.xml
+++ b/pom.xml
@@ -6,7 +6,7 @@

    <groupId>ink.numberone</groupId>
    <artifactId>smartjavaai-parent</artifactId>
-    <version>1.0.5</version>
+    <version>1.0.6</version>
    <packaging>pom</packaging>
    <description>SmartJavaAI</description>
    <modules>
@@ -35,13 +35,13 @@
            <dependency>
                <groupId>ink.numberone</groupId>
                <artifactId>smartjavaai-common</artifactId>
-                <version>1.0.5</version>
+                <version>1.0.6</version>
            </dependency>

            <dependency>
                <groupId>ink.numberone</groupId>
                <artifactId>smartjavaai-face</artifactId>
-                <version>1.0.5</version>
+                <version>1.0.6</version>
            </dependency>
        </dependencies>

--- a/smartjavaai-common/pom.xml
+++ b/smartjavaai-common/pom.xml
@@ -6,7 +6,7 @@
    <parent>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-parent</artifactId>
-        <version>1.0.5</version>
+        <version>1.0.6</version>
    </parent>

    <artifactId>smartjavaai-common</artifactId>
--- a/smartjavaai-face/pom.xml
+++ b/smartjavaai-face/pom.xml
@@ -6,11 +6,11 @@
    <parent>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-parent</artifactId>
-        <version>1.0.5</version>
+        <version>1.0.6</version>
    </parent>

    <artifactId>smartjavaai-face</artifactId>
-    <version>1.0.5</version>
+    <version>1.0.6</version>
    <name>smartjavaai-face</name>
    <description>SmartJavaAI</description>
    <url>https://github.com/geekwenjie/SmartJavaAI</url>
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/FeatureExtractionAlgo.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/algo/FeatureExtractionAlgo.java
@@ -1,5 +1,6 @@
 package cn.smartjavaai.face.algo;

+import ai.djl.Device;
 import ai.djl.inference.Predictor;
 import ai.djl.modality.cv.Image;
 import ai.djl.modality.cv.ImageFactory;
@@ -14,6 +15,7 @@ import cn.smartjavaai.face.AbstractFaceAlgorithm;
 import cn.smartjavaai.face.FaceDetectedResult;
 import cn.smartjavaai.face.FaceDetectionTranslator;
 import cn.smartjavaai.face.ModelConfig;
+import cn.smartjavaai.face.translator.FaceFeatureTranslator;
 import org.apache.commons.lang3.StringUtils;

 import java.io.InputStream;
@@ -50,32 +52,34 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
     * 加载人脸特征提取模型
     * @param config
     * @throws Exception
-     *//*
+     */
    @Override
    public void loadFaceFeatureModel(ModelConfig config) throws Exception {
        String normalize = mean.stream().map(Object::toString).collect(Collectors.joining(","));
-        faceFeatureCriteria = Criteria.builder()
+        faceFeatureCriteria =
+                Criteria.builder()
                        .setTypes(Image.class, float[].class)
+                        .optModelName("face_feature") // specify model file prefix
                        .optModelUrls(StringUtils.isNotBlank(config.getModelPath()) ? null :
                                "https://resources.djl.ai/test-models/pytorch/face_feature.zip")
                        .optModelPath(StringUtils.isNotBlank(config.getModelPath()) ? Paths.get(config.getModelPath()) : null)
-                        .optModelName("face_feature") // specify model file prefix
+                        .optTranslator(new FaceFeatureTranslator())
                        .optArgument("normalize", normalize)
-                        .optTranslatorFactory(new ImageFeatureExtractorFactory())
-                        .optProgress(new ProgressBar())
                        .optEngine("PyTorch") // Use PyTorch engine
+                        .optProgress(new ProgressBar())
                        .build();
+
        model = faceFeatureCriteria.loadModel();
        predictor = model.newPredictor();
    }


-    *//**
+    /**
     * 特征提取
     * @param imagePath 图片路径
     * @return
     * @throws Exception
-     *//*
+     */
    @Override
    public float[] featureExtraction(String imagePath) throws Exception {
        Path imageFile = Paths.get(imagePath);
@@ -84,12 +88,12 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
        return predictor.predict(img);
    }

-    *//**
+    /**
     * 特征提取
     * @param inputStream 输入流
     * @return
     * @throws Exception
-     *//*
+     */
    @Override
    public float[] featureExtraction(InputStream inputStream) throws Exception {
        Image img = ImageFactory.getInstance().fromInputStream(inputStream);
@@ -97,13 +101,13 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
        return predictor.predict(img);
    }

-    *//**
+    /**
     * 计算相似度
     * @param feature1 图1特征
     * @param feature2 图2特征
     * @return
     * @throws Exception
-     *//*
+     */
    @Override
    public float calculSimilar(float[] feature1, float[] feature2) throws Exception {
        float ret = 0.0f;
@@ -118,13 +122,13 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
        return (float) ((ret / Math.sqrt(mod1) / Math.sqrt(mod2) + 1) / 2.0f);
    }

-    *//**
+    /**
     * 特征比较
     * @param imagePath1 图1路径
     * @param imagePath2 图2路径
     * @return
     * @throws Exception
-     *//*
+     */
    @Override
    public float featureComparison(String imagePath1, String imagePath2) throws Exception {
        float[] feature1 = featureExtraction(imagePath1);
@@ -132,19 +136,19 @@ public class FeatureExtractionAlgo extends AbstractFaceAlgorithm {
        return calculSimilar(feature1, feature2);
    }

-    *//**
+    /**
     * 特征比较
     * @param inputStream1 图1输入流
     * @param inputStream2 图2输入流
     * @return
     * @throws Exception
-     *//*
+     */
    @Override
    public float featureComparison(InputStream inputStream1, InputStream inputStream2) throws Exception {
        float[] feature1 = featureExtraction(inputStream1);
        float[] feature2 = featureExtraction(inputStream2);
        return calculSimilar(feature1, feature2);
-    }*/
+    }

    /*@Override
    public float[] recognize(FaceRegion region) {
--- a/smartjavaai-face/src/main/java/cn/smartjavaai/face/translator/FaceFeatureTranslator.java
+++ b/smartjavaai-face/src/main/java/cn/smartjavaai/face/translator/FaceFeatureTranslator.java
@@ -0,0 +1,52 @@
+package cn.smartjavaai.face.translator;
+
+import ai.djl.modality.cv.Image;
+import ai.djl.modality.cv.transform.Normalize;
+import ai.djl.modality.cv.transform.Resize;
+import ai.djl.modality.cv.transform.ToTensor;
+import ai.djl.ndarray.NDArray;
+import ai.djl.ndarray.NDList;
+import ai.djl.translate.Batchifier;
+import ai.djl.translate.Pipeline;
+import ai.djl.translate.Translator;
+import ai.djl.translate.TranslatorContext;
+
+/**
+ * @author 邓文杰
+ * @date 2025/3/31
+ */
+public final class FaceFeatureTranslator implements Translator<Image, float[]> {
+
+    public FaceFeatureTranslator() {
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    @Override
+    public NDList processInput(TranslatorContext ctx, Image input) {
+        NDArray array = input.toNDArray(ctx.getNDManager(), Image.Flag.COLOR);
+        Pipeline pipeline = new Pipeline();
+        pipeline
+                .add(new Resize(180))
+                .add(new ToTensor())
+                .add(new Normalize(
+                        new float[]{127.5f / 255.0f, 127.5f / 255.0f, 127.5f / 255.0f},
+                        new float[]{128.0f / 255.0f, 128.0f / 255.0f, 128.0f / 255.0f}));
+
+        return pipeline.transform(new NDList(array));
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    @Override
+    public float[] processOutput(TranslatorContext ctx, NDList list) {
+        return list.singletonOrThrow().toFloatArray();
+    }
+
+    @Override
+    public Batchifier getBatchifier() {
+        return Batchifier.STACK;
+    }
+}
--- a/smartjavaai-face/src/main/java/com/seetaface/NativeLoader.java
+++ b/smartjavaai-face/src/main/java/com/seetaface/NativeLoader.java
@@ -1,11 +1,15 @@
 package com.seetaface;


+import jdk.dynalink.linker.support.Lookup;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.StringUtils;

 import java.io.*;
+import java.lang.invoke.MethodHandles;
+import java.lang.invoke.VarHandle;
 import java.lang.reflect.Field;
+import java.lang.reflect.Method;
 import java.nio.file.Files;
 import java.nio.file.Path;
 import java.nio.file.StandardCopyOption;
@@ -19,9 +23,10 @@ public class NativeLoader {


    private static Path tempNativeDir;
-    private static final String[] WIN_LIBS = {"tennis","tennis_haswell","tennis_pentium","tennis_sandy_bridge","SeetaAuthorize","SeetaFaceAntiSpoofingX600","SeetaFaceDetector600","SeetaFaceLandmarker600","SeetaFaceRecognizer610","SeetaFace6JNI"};
-    private static final String[] LINUX_CENTOS_LIBS = {"libmain.so"};
-    private static final String[] LINUX_UBUNTU_LIBS = {"libdependency1.so", "libdependency2.so", "libmain.so"};
+    private static final String[] WIN_LIBS = {"tennis.dll","tennis_haswell.dll","tennis_pentium.dll","tennis_sandy_bridge.dll","SeetaAuthorize.dll","SeetaFaceAntiSpoofingX600.dll","SeetaFaceDetector600.dll","SeetaFaceLandmarker600.dll","SeetaFaceRecognizer610.dll","SeetaFace6JNI.dll"};
+    //private static final String[] WIN_LIBS = {"tennis","tennis_haswell","tennis_pentium","tennis_sandy_bridge","SeetaAuthorize","SeetaFaceAntiSpoofingX600","SeetaFaceDetector600","SeetaFaceLandmarker600","SeetaFaceRecognizer610","SeetaFace6JNI"};
+    private static final String[] LINUX_CENTOS_LIBS = {"libSeetaAuthorize.so","libtennis.so","libtennis_haswell.so","libtennis_pentium.so","libtennis_sandy_bridge.so","libSeetaFaceDetector600.so","libSeetaAgePredictor600.so","libSeetaEyeStateDetector200.so","libSeetaFaceAntiSpoofingX600.so","libSeetaFaceLandmarker600.so","libSeetaFaceRecognizer610.so","libSeetaGenderPredictor600.so","libSeetaMaskDetector200.so","libSeetaPoseEstimation600.so","libSeetaFaceTracking600.so","libSeetaQualityAssessor300.so"};
+    private static final String[] LINUX_UBUNTU_LIBS = {"libSeetaAuthorize.so","libtennis.so","libtennis_haswell.so","libtennis_pentium.so","libtennis_sandy_bridge.so","libSeetaFaceDetector600.so","libSeetaAgePredictor600.so","libSeetaEyeStateDetector200.so","libSeetaFaceAntiSpoofingX600.so","libSeetaFaceLandmarker600.so","libSeetaFaceRecognizer610.so","libSeetaGenderPredictor600.so","libSeetaMaskDetector200.so","libSeetaPoseEstimation600.so","libSeetaFaceTracking600.so","libSeetaQualityAssessor300.so"};

    private static final String TEMP_DIR = "smartjavaai-native-libs";

@@ -51,18 +56,12 @@ public class NativeLoader {
            } else {
                System.setProperty("java.library.path", sysLib + separator + tempNativeDir);
            }
-            try {
-                //使java.library.path生效
-                Field sysPathsField = ClassLoader.class.getDeclaredField("sys_paths");
-                sysPathsField.setAccessible(true);
-                sysPathsField.set(null, null);
-            } catch (NoSuchFieldException | IllegalAccessException e) {
-                e.printStackTrace();
-            }
+

            // 按顺序加载库（确保依赖关系）
            for (String libName : libNames) {
-                System.loadLibrary(libName);
+                log.info("Loading library: " + tempNativeDir + File.separator + libName);
+                System.load(tempNativeDir + File.separator + libName);
            }
        } catch (Exception e) {
            throw new RuntimeException("Native library loading failed", e);
@@ -84,7 +83,7 @@ public class NativeLoader {
     * @throws IOException
     */
    private static void extractLibrary(String libName,String libDir) throws IOException {
-        String resourcePath = "/native" + libDir + "/" + libName + ".dll";
+        String resourcePath = "/native" + libDir + "/" + libName;
        try (InputStream in = NativeLoader.class.getResourceAsStream(resourcePath)) {
            if (in == null) throw new FileNotFoundException(resourcePath);

@@ -107,7 +106,7 @@ public class NativeLoader {
        String osName = System.getProperty("os.name").toLowerCase();
        if (osName.contains("win")) {
            return "/windows";
-        } else if (osName.contains("linux")) {
+        } /*else if (osName.contains("linux")) {
            String linuxOsName = getLinuxOsName();
            if(StringUtils.isBlank(linuxOsName)){
                throw new UnsupportedOperationException("Unsupported platform");
@@ -117,7 +116,7 @@ public class NativeLoader {
            }else if(linuxOsName.contains("centos")){
                return "/linux/centos";
            }
-        }
+        }*/
        throw new UnsupportedOperationException("Unsupported platform");
    }

--- a/smartjavaai-seetaface6-lib/pom.xml
+++ b/smartjavaai-seetaface6-lib/pom.xml
@@ -6,7 +6,7 @@
    <parent>
        <groupId>ink.numberone</groupId>
        <artifactId>smartjavaai-parent</artifactId>
-        <version>1.0.5</version>
+        <version>1.0.6</version>
    </parent>

    <artifactId>smartjavaai-seetaface6-lib</artifactId>
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaAgePredictor600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaAgePredictor600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaAuthorize.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaAuthorize.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaEyeStateDetector200.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaEyeStateDetector200.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceAntiSpoofingX600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceAntiSpoofingX600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceDetector600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceDetector600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceLandmarker600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceLandmarker600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceRecognizer610.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceRecognizer610.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceTracking600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaFaceTracking600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaGenderPredictor600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaGenderPredictor600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaMaskDetector200.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaMaskDetector200.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaPoseEstimation600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaPoseEstimation600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaQualityAssessor300.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libSeetaQualityAssessor300.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis_haswell.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis_haswell.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis_pentium.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis_pentium.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis_sandy_bridge.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/centos/libtennis_sandy_bridge.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaAgePredictor600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaAgePredictor600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaAuthorize.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaAuthorize.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaEyeStateDetector200.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaEyeStateDetector200.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceAntiSpoofingX600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceAntiSpoofingX600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceDetector600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceDetector600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceLandmarker600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceLandmarker600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceRecognizer610.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceRecognizer610.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceTracking600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaFaceTracking600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaGenderPredictor600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaGenderPredictor600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaMaskDetector200.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaMaskDetector200.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaPoseEstimation600.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaPoseEstimation600.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaQualityAssessor300.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libSeetaQualityAssessor300.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis_haswell.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis_haswell.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis_pentium.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis_pentium.so
--- a/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis_sandy_bridge.so
+++ b/smartjavaai-seetaface6-lib/src/main/resources/native/linux/ubuntu/libtennis_sandy_bridge.so