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如何用DL4J构建起一个人脸识别系统

2020-09-16    
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一、概述

人脸识别本质上是一个求相似度的问题,相同的人脸映射到同一个空间,他们的距离比较近,这个距离的度量可以是余弦距离,也可以是欧几里得距离,或者其他的距离。下面有三个头像。

 

如何用DL4J构建起一个人脸识别系统

 

 

如何用DL4J构建起一个人脸识别系统

 

 

如何用DL4J构建起一个人脸识别系统

 

A B C

显然A和C是相同人脸,A和B是不同人脸,用数学怎么描述呢?假设有个距离函数d(x1,x2),那么 d(A,B) > d(A,C)。在真实的人脸识别应用中,函数d(x1,x2)小到一个什么范围才认定为同一张人脸呢?这个值和训练模型时的参数有关,这个将在下文中给出。值得注意的是,如果函数d为cosine,则值越大表示越相似。一个通用的人脸识别模型应该包含特征提取(也就是特征映射)和距离计算两个单元。

二、构造模型

那么有什么办法可以特征映射呢?对于图像的处理,卷积神经网络无疑是目前最优的办法。DeepLearning4J已经内置了训练好的VggFace模型,是基于vgg16训练的。vggFace的下载地址:https://dl4jdata.blob.core.windows.net/models/vgg16_dl4j_vggface_inference.v1.zip,这个地址是怎么获取到的呢?直接跟一下源码VGG16,pretrainedUrl方法里的DL4JResources.getURLString方法便有相关模型的下载地址,VGG19、ResNet50等等pretrained的模型下载地址,都可以这样找到。源码如下

public class VGG16 extends ZooModel {
    @Builder.Default private long seed = 1234;
    @Builder.Default private int[] inputShape = new int[] {3, 224, 224};
    @Builder.Default private int numClasses = 0;
    @Builder.Default private IUpdater updater = new Nesterovs();
    @Builder.Default private CacheMode cacheMode = CacheMode.NONE;
    @Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
    @Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
    private VGG16() {}
    @Override
    public String pretrainedUrl(PretrainedType pretrainedType) {
        if (pretrainedType == PretrainedType.IMAGENET)
            return DL4JResources.getURLString("models/vgg16_dl4j_inference.zip");
        else if (pretrainedType == PretrainedType.CIFAR10)
            return DL4JResources.getURLString("models/vgg16_dl4j_cifar10_inference.v1.zip");
        else if (pretrainedType == PretrainedType.VGGFACE)
            return DL4JResources.getURLString("models/vgg16_dl4j_vggface_inference.v1.zip");
        else
            return null;
    }

vgg16的模型结构如下:

====================================================================================================
VertexName (VertexType)        nIn,nOut     TotalParams   ParamsShape                  Vertex Inputs
====================================================================================================
input_1 (InputVertex)          -,-          -             -                            -            
conv1_1 (ConvolutionLayer)     3,64         1,792         W:{64,3,3,3}, b:{1,64}       [input_1]    
conv1_2 (ConvolutionLayer)     64,64        36,928        W:{64,64,3,3}, b:{1,64}      [conv1_1]    
pool1 (SubsamplingLayer)       -,-          0             -                            [conv1_2]    
conv2_1 (ConvolutionLayer)     64,128       73,856        W:{128,64,3,3}, b:{1,128}    [pool1]      
conv2_2 (ConvolutionLayer)     128,128      147,584       W:{128,128,3,3}, b:{1,128}   [conv2_1]    
pool2 (SubsamplingLayer)       -,-          0             -                            [conv2_2]    
conv3_1 (ConvolutionLayer)     128,256      295,168       W:{256,128,3,3}, b:{1,256}   [pool2]      
conv3_2 (ConvolutionLayer)     256,256      590,080       W:{256,256,3,3}, b:{1,256}   [conv3_1]    
conv3_3 (ConvolutionLayer)     256,256      590,080       W:{256,256,3,3}, b:{1,256}   [conv3_2]    
pool3 (SubsamplingLayer)       -,-          0             -                            [conv3_3]    
conv4_1 (ConvolutionLayer)     256,512      1,180,160     W:{512,256,3,3}, b:{1,512}   [pool3]      
conv4_2 (ConvolutionLayer)     512,512      2,359,808     W:{512,512,3,3}, b:{1,512}   [conv4_1]    
conv4_3 (ConvolutionLayer)     512,512      2,359,808     W:{512,512,3,3}, b:{1,512}   [conv4_2]    
pool4 (SubsamplingLayer)       -,-          0             -                            [conv4_3]    
conv5_1 (ConvolutionLayer)     512,512      2,359,808     W:{512,512,3,3}, b:{1,512}   [pool4]      
conv5_2 (ConvolutionLayer)     512,512      2,359,808     W:{512,512,3,3}, b:{1,512}   [conv5_1]    
conv5_3 (ConvolutionLayer)     512,512      2,359,808     W:{512,512,3,3}, b:{1,512}   [conv5_2]    
pool5 (SubsamplingLayer)       -,-          0             -                            [conv5_3]    
flatten (PreprocessorVertex)   -,-          -             -                            [pool5]      
fc6 (DenseLayer)               25088,4096   102,764,544   W:{25088,4096}, b:{1,4096}   [flatten]    
fc7 (DenseLayer)               4096,4096    16,781,312    W:{4096,4096}, b:{1,4096}    [fc6]        
fc8 (DenseLayer)               4096,2622    10,742,334    W:{4096,2622}, b:{1,2622}    [fc7]        
----------------------------------------------------------------------------------------------------
            Total Parameters:  145,002,878
        Trainable Parameters:  145,002,878
           Frozen Parameters:  0

对于VggFace我们只需要前面的卷积层和池化层来提取特征,其他的全连接层可以丢弃掉,那么我们的模型可以设置成如下的样子。

如何用DL4J构建起一个人脸识别系统

 

说明:这里用StackVertex和UnStackVertex的原因是,dl4j中默认情况下有都给输入时是把张量Merge在一起输入的,达不到多个输入共享权重的目的,所以这里先用StackVertex沿着第0维堆叠张量,共享卷积和池化提取特征,再用UnStackVertex拆开张量,给后面用于计算距离用。

接下来的问题是,dl4j中迁移学习api只能在模型尾部追加相关的结构,而现在我们的场景是把pretrained的模型的部分结构放在中间,怎么办呢?不着急,我们看看迁移学习API的源码,看DL4J是怎么封装的。在org.deeplearning4j.nn.transferlearning.TransferLearning的build方法中找到了蛛丝马迹。

public ComputationGraph build() {
            initBuilderIfReq();            ComputationGraphConfiguration newConfig = editedConfigBuilder                    .validateOutputLayerConfig(validateOutputLayerConfig == null ? true : validateOutputLayerConfig).build();
            if (this.workspaceMode != null)
                newConfig.setTrainingWorkspaceMode(workspaceMode);            ComputationGraph newGraph = new ComputationGraph(newConfig);
            newGraph.init();            int[] topologicalOrder = newGraph.topologicalSortOrder();            org.deeplearning4j.nn.graph.vertex.GraphVertex[] vertices = newGraph.getVertices();            if (!editedVertices.isEmpty()) {
                //set params from orig graph as necessary to new graph
                for (int i = 0; i < topologicalOrder.length; i++) {
                    if (!vertices[topologicalOrder[i]].hasLayer())
                        continue;
                    org.deeplearning4j.nn.api.Layer layer = vertices[topologicalOrder[i]].getLayer();                    String layerName = vertices[topologicalOrder[i]].getVertexName();                    long range = layer.numParams();                    if (range <= 0)
                        continue; //some layers have no params
                    if (editedVertices.contains(layerName))
                        continue; //keep the changed params
                    INDArray origParams = origGraph.getLayer(layerName).params();                    layer.setParams(origParams.dup()); //copy over origGraph params
                }            } else {
                newGraph.setParams(origGraph.params());            }

原来是直接调用 layer.setParams方法,给每一个层set相关的参数即可。接下来,我们就有思路了,直接构造一个和vgg16一样的模型,把vgg16的参数set到新的模型里即可。其实本质上,DeepLearning被train之后,有用的就是参数而已,有了这些参数,我们就可以随心所欲的用这些模型了。废话不多说,我们直接上代码,构建我们目标模型

private static ComputationGraph buildModel() {
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(123)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).activation(Activation.RELU)                .graphBuilder().addInputs("input1", "input2").addVertex("stack", new StackVertex(), "input1", "input2")
                .layer("conv1_1",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nIn(3).nOut(64)
                                .build(),                        "stack")
                .layer("conv1_2",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(64).build(),
                        "conv1_1")
                .layer("pool1",
                        new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
                                .stride(2, 2).build(),
                        "conv1_2")
                // block 2
                .layer("conv2_1",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(128).build(),
                        "pool1")
                .layer("conv2_2",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(128).build(),
                        "conv2_1")
                .layer("pool2",
                        new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
                                .stride(2, 2).build(),
                        "conv2_2")
                // block 3
                .layer("conv3_1",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(256).build(),
                        "pool2")
                .layer("conv3_2",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(256).build(),
                        "conv3_1")
                .layer("conv3_3",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(256).build(),
                        "conv3_2")
                .layer("pool3",
                        new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
                                .stride(2, 2).build(),
                        "conv3_3")
                // block 4
                .layer("conv4_1",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(512).build(),
                        "pool3")
                .layer("conv4_2",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(512).build(),
                        "conv4_1")
                .layer("conv4_3",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(512).build(),
                        "conv4_2")
                .layer("pool4",
                        new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
                                .stride(2, 2).build(),
                        "conv4_3")
                // block 5
                .layer("conv5_1",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(512).build(),
                        "pool4")
                .layer("conv5_2",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(512).build(),
                        "conv5_1")
                .layer("conv5_3",
                        new ConvolutionLayer.Builder().kernelSize(3, 3).stride(1, 1).padding(1, 1).nOut(512).build(),
                        "conv5_2")
                .layer("pool5",
                        new SubsamplingLayer.Builder().poolingType(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2)
                                .stride(2, 2).build(),
                        "conv5_3")
                .addVertex("unStack1", new UnstackVertex(0, 2), "pool5")
                .addVertex("unStack2", new UnstackVertex(1, 2), "pool5")
                .addVertex("cosine", new CosineLambdaVertex(), "unStack1", "unStack2")
                .addLayer("out", new LossLayer.Builder().build(), "cosine").setOutputs("out")
                .setInputTypes(InputType.convolutionalFlat(224, 224, 3), InputType.convolutionalFlat(224, 224, 3))
                .build();        ComputationGraph network = new ComputationGraph(conf);
        network.init();        return network;
    }

接下来读取VGG16的参数,set到我们的新模型里。为了代码方便,我们将LayerName设定的和vgg16里一样

String vggLayerNames = "conv1_1,conv1_2,conv2_1,conv2_2,conv3_1,conv3_2,conv3_3,conv4_1,conv4_2,conv4_3,conv5_1,conv5_2,conv5_3"; 
File vggfile = new File("F:/vgg16_dl4j_vggface_inference.v1.zip");
        ComputationGraph vggFace =                ModelSerializer.restoreComputationGraph(vggfile);        ComputationGraph model = buildModel();        for (String name : vggLayerNames.split(",")) {
            model.getLayer(name).setParams(vggFace.getLayer(name).params().dup());		}

特征提取层构造完毕,提取特征之后,我们要计算距离了,这里就需要用DL4J实现自定义层,DL4J提供的自动微分可以非常方便的实现自定义层,这里我们选择 SameDiffLambdaVertex,原因是这一层不需要任何参数,仅仅计算cosine即可,代码如下:

public class CosineLambdaVertex extends SameDiffLambdaVertex {
	@Override
	public SDVariable defineVertex(SameDiff sameDiff, VertexInputs inputs) {
		SDVariable input1 = inputs.getInput(0);
		SDVariable input2 = inputs.getInput(1);
		return sameDiff.expandDims(sameDiff.math.cosineSimilarity(input1, input2, 1, 2, 3), 1);
	}	@Override
	public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException {
		return InputType.feedForward(1);
	}}

说明:计算cosine之后这里用expandDims将一维张量拓宽成二维,是为了在LFW数据集中验证模型的准确性。

DL4J也提供其他的自定层和自定义节点的实现,一共有如下五种:

  1. Layers: standard single input, single output layers defined using SameDiff. To implement, extend org.deeplearning4j.nn.conf.layers.samediff.SameDiffLayer
  2. Lambda layers: as above, but without any parameters. You only need to implement a single method for these! To implement, extend org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaLayer
  3. Graph vertices: multiple inputs, single output layers usable only in ComputationGraph. To implement: extend org.deeplearning4j.nn.conf.layers.samediff.SameDiffVertex
  4. Lambda vertices: as above, but without any parameters. Again, you only need to implement a single method for these! To implement, extend org.deeplearning4j.nn.conf.layers.samediff.SameDiffLambdaVertex
  5. Output layers: An output layer, for calculating scores/losses. Used as the final layer in a network. To implement, extend org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer

案例地址:https://github.com/eclipse/deeplearning4j-examples/tree/master/samediff-examples

说明文档:https://github.com/eclipse/deeplearning4j-examples/blob/master/samediff-examples/src/main/JAVA/org/nd4j/examples/samediff/customizingdl4j/README.md

接下来,还有最后一个问题,输出层怎么定义?输出层不需要任何参数和计算,仅仅将cosine结果输出即可,dl4j中提供LossLayer天然满足这种结构,没有参数,且激活函数为恒等函数IDENTITY。那么到此为止模型构造完成,最终结构如下:

=========================================================================================================
VertexName (VertexType)        nIn,nOut   TotalParams   ParamsShape                  Vertex Inputs       
=========================================================================================================
input1 (InputVertex)           -,-        -             -                            -                   
input2 (InputVertex)           -,-        -             -                            -                   
stack (StackVertex)            -,-        -             -                            [input1, input2]    
conv1_1 (ConvolutionLayer)     3,64       1,792         W:{64,3,3,3}, b:{1,64}       [stack]             
conv1_2 (ConvolutionLayer)     64,64      36,928        W:{64,64,3,3}, b:{1,64}      [conv1_1]           
pool1 (SubsamplingLayer)       -,-        0             -                            [conv1_2]           
conv2_1 (ConvolutionLayer)     64,128     73,856        W:{128,64,3,3}, b:{1,128}    [pool1]             
conv2_2 (ConvolutionLayer)     128,128    147,584       W:{128,128,3,3}, b:{1,128}   [conv2_1]           
pool2 (SubsamplingLayer)       -,-        0             -                            [conv2_2]           
conv3_1 (ConvolutionLayer)     128,256    295,168       W:{256,128,3,3}, b:{1,256}   [pool2]             
conv3_2 (ConvolutionLayer)     256,256    590,080       W:{256,256,3,3}, b:{1,256}   [conv3_1]           
conv3_3 (ConvolutionLayer)     256,256    590,080       W:{256,256,3,3}, b:{1,256}   [conv3_2]           
pool3 (SubsamplingLayer)       -,-        0             -                            [conv3_3]           
conv4_1 (ConvolutionLayer)     256,512    1,180,160     W:{512,256,3,3}, b:{1,512}   [pool3]             
conv4_2 (ConvolutionLayer)     512,512    2,359,808     W:{512,512,3,3}, b:{1,512}   [conv4_1]           
conv4_3 (ConvolutionLayer)     512,512    2,359,808     W:{512,512,3,3}, b:{1,512}   [conv4_2]           
pool4 (SubsamplingLayer)       -,-        0             -                            [conv4_3]           
conv5_1 (ConvolutionLayer)     512,512    2,359,808     W:{512,512,3,3}, b:{1,512}   [pool4]             
conv5_2 (ConvolutionLayer)     512,512    2,359,808     W:{512,512,3,3}, b:{1,512}   [conv5_1]           
conv5_3 (ConvolutionLayer)     512,512    2,359,808     W:{512,512,3,3}, b:{1,512}   [conv5_2]           
pool5 (SubsamplingLayer)       -,-        0             -                            [conv5_3]           
unStack1 (UnstackVertex)       -,-        -             -                            [pool5]             
unStack2 (UnstackVertex)       -,-        -             -                            [pool5]             
cosine (SameDiffGraphVertex)   -,-        -             -                            [unStack1, unStack2]
out (LossLayer)                -,-        0             -                            [cosine]            
---------------------------------------------------------------------------------------------------------
            Total Parameters:  14,714,688
        Trainable Parameters:  14,714,688
           Frozen Parameters:  0
=========================================================================================================

三、在LFW上验证模型准确率

LFW数据下载地址:http://vis-www.cs.umass.edu/lfw/,我下载之后放在了F:facerecognition目录下。

构造测试集,分别构造正例和负例,将相同的人脸放一堆,不同的人脸放一堆,代码如下:

import org.Apache.commons.io.FileUtils;
import java.io.File;
import java.io.IOException;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
public class DataTools {
    private static final String PARENT_PATH = "F:/facerecognition";
    public static void main(String[] args) throws IOException {
        File file = new File(PARENT_PATH + "/lfw");
        List<File> list = Arrays.asList(file.listFiles());
        for (int i = 0; i < list.size(); i++) {
            String name = list.get(i).getName();
            File[] faceFileArray = list.get(i).listFiles();
            if (null == faceFileArray) {
                continue;
            }            //构造正例
            if (faceFileArray.length > 1) {
                String positiveFilePath = PARENT_PATH + "/pairs/1/" + name;
                File positiveFileDir = new File(positiveFilePath);
                if (positiveFileDir.exists()) {
                    positiveFileDir.delete();
                }
                positiveFileDir.mkdir();
                FileUtils.copyFile(faceFileArray[0], new File(positiveFilePath + "/" + faceFileArray[0].getName()));
                FileUtils.copyFile(faceFileArray[1], new File(positiveFilePath + "/" + faceFileArray[1].getName()));
            }
            //构造负例
            String negativeFilePath = PARENT_PATH + "/pairs/0/" + name;
            File negativeFileDir = new File(negativeFilePath);
            if (negativeFileDir.exists()) {
                negativeFileDir.delete();
            }
            negativeFileDir.mkdir();
            FileUtils.copyFile(faceFileArray[0], new File(negativeFilePath + "/" + faceFileArray[0].getName()));
            File[] differentFaceArray = list.get(randomInt(list.size(), i)).listFiles();
            int differentFaceIndex = randomInt(differentFaceArray.length, -1);
            FileUtils.copyFile(differentFaceArray[differentFaceIndex], new File(negativeFilePath + "/" + differentFaceArray[differentFaceIndex].getName()));
        }
    }
    public static int randomInt(int max, int target) {
        Random random = new Random();
        while (true) {
            int result = random.nextInt(max);
            if (result != target) {
                return result;
            }
        }
    }
}

测试集构造完成之后,构造迭代器,迭代器中读取图片用了NativeImageLoader,在《如何利用deeplearning4j中datavec对图像进行处理》有相关介绍。

public class DataSetForEvaluation implements MultiDataSetIterator {
	private List<FacePair> facePairList;
	private int batchSize;
	private int totalBatches;
	private NativeImageLoader imageLoader;
	private int currentBatch = 0;
	public DataSetForEvaluation(List<FacePair> facePairList, int batchSize) {
		this.facePairList = facePairList;
		this.batchSize = batchSize;
		this.totalBatches = (int) Math.ceil((double) facePairList.size() / batchSize);
		this.imageLoader = new NativeImageLoader(224, 224, 3, new ResizeImageTransform(224, 224));
	}	@Override
	public boolean hasNext() {
		return currentBatch < totalBatches;
	}	@Override
	public MultiDataSet next() {
		return next(batchSize);
	}	@Override
	public MultiDataSet next(int num) {
		int i = currentBatch * batchSize;
		int currentBatchSize = Math.min(batchSize, facePairList.size() - i);
		INDArray input1 = Nd4j.zeros(currentBatchSize, 3,224,224);
		INDArray input2 =  Nd4j.zeros(currentBatchSize, 3,224,224);
		INDArray label = Nd4j.zeros(currentBatchSize, 1);
		for (int j = 0; j < currentBatchSize; j++) {
			try {
				input1.put(new INDArrayIndex[]{NDArrayIndex.point(j),NDArrayIndex.all(),NDArrayIndex.all(),NDArrayIndex.all()}, imageLoader.asMatrix(facePairList.get(i).getList().get(0)).div(255));
				input2.put(new INDArrayIndex[]{NDArrayIndex.point(j),NDArrayIndex.all(),NDArrayIndex.all(),NDArrayIndex.all()},imageLoader.asMatrix(facePairList.get(i).getList().get(1)).div(255));
			} catch (Exception e) {
				e.printStackTrace();			}			label.putScalar((long) j, 0, facePairList.get(i).getLabel());
			++i;		}		System.out.println(currentBatch);		++currentBatch;		return new org.nd4j.linalg.dataset.MultiDataSet(new INDArray[] { input1, input2},
				new INDArray[] { label });
	}	@Override
	public void setPreProcessor(MultiDataSetPreProcessor preProcessor) {
	}	@Override
	public MultiDataSetPreProcessor getPreProcessor() {
		return null;
	}	@Override
	public boolean resetSupported() {
		return true;
	}	@Override
	public boolean asyncSupported() {
		return false;
	}	@Override
	public void reset() {
		currentBatch = 0;
	}}

接下来可以评估模型的性能了,准确率和精确率还凑合,但F1值有点低。

========================Evaluation Metrics========================
 # of classes:    2
 Accuracy:        0.8973
 Precision:       0.9119
 Recall:          0.6042
 F1 Score:        0.7268
Precision, recall & F1: reported for positive class (class 1 - "1") only
=========================Confusion Matrix=========================
    0    1
-----------
 5651   98 | 0 = 0
  665 1015 | 1 = 1
Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
==================================================================

四、用SpringBoot将模型封装成服务

模型保存之后,就是一堆死参数,怎么变成线上的服务呢?人脸识别服务分为两种1:1和1:N

1、1:1应用

典型的1:1应用如手机的人脸识别解锁,钉钉的人脸识别考勤,这种应用比较简单,仅仅只需要张三是张三即可,运算量很小。很容易实现

2、1:N应用

典型的1:N应用如公安机关的人脸找人,在不知道目标人脸身份的前提下,从海量人脸库中找到目标人脸是谁。当人脸库中数据量巨大的时候,计算是一个很大的问题。

如果不要求结构可以实时出来,可以离线用Hadoop MapReduce或者Spark来计算一把,我们需要做的工作仅仅是封装一个Hive UDF函数、或者MapReduce jar,再或者是Spark RDD编程即可。

但对于要求计算结果实时性,这个问题不能转化为一个索引问题,所以需要设计一种计算框架,可以分布式的解决全局Max或者全局Top的问题,大致结构如下:

如何用DL4J构建起一个人脸识别系统

 

蓝色箭头表示请求留向,绿色箭头表示计算结果返回,图中描述了一个客户端请求打到了节点Node3上,由Node3转发请求到其他Node,并行计算。当然如果各个Node内存够大,可以将整个人脸库的张量都预热到内存常驻,加快计算速度。

当然,本篇博客中并没有实现并行计算框架,只实现了用springboot将模型包装成服务。运行FaceRecognitionApplication,访问http://localhost:8080/index,服务效果如下:

如何用DL4J构建起一个人脸识别系统

 

本篇博客的所有代码:https://gitee.com/lxkm/dl4j-demo/tree/master/face-recognition

五、总结

本篇博客的主要意图是介绍如何把DL4J用于实战,包括pretrained模型参数的获取、自定义层的实现,自定义迭代器的实现,用springboot包装层服务等等。

当然一个人脸识别系统只有一个图片embedding和求张量距离是不够的,还应该包括人脸矫正、抵御AI attack(后面的博客也会介绍如何用DL4J进行 FGSM 攻击)、人脸关键部位特征提取等等很多精细化的工作要做。当然要把人脸识别做成一个通用SAAS服务,也是有很多工作要做。

要训练一个好的人脸识别模型,需要多种loss function的配合,如可以先用SoftMax做分类,再用Center Loss、Triple Loss做微调,后续的博客中将介绍如何用DL4J实现Triple Loss(

如何用DL4J构建起一个人脸识别系统

 

),来训练人脸识别模型。

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此博客乃作者原创,出处:https://my.oschina.net/u/1778239/blog/4575155

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