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Face Recognition V1.0(TrialFaceSDK)

Capability Introduction

Interface Capability

  • Face Recognition(Feature Extraction): input face image of 3\times 128\times 128(channels \times height \times width), extract 128-dimension feature vector.

Invoke Method

Example Code：extract 128-dimension feature vector

package com.glasssix;
import com.glasssix.Gaiulinya.Gaiulinya;

import org.opencv.core.*;
import org.opencv.highgui.HighGui;
import org.opencv.imgcodecs.Imgcodecs;
import org.opencv.imgproc.Imgproc;

public class Main {

    public static void main(String[] args) {
        System.loadLibrary("opencv_java400");
        //using CPU when device is less than zero, otherwise use the GPU numbered by 'device', device is set to -1 by default.
		int device = -1;
		//order==0(NCHW),others(NHWC), order is set to zero by default.
		int order = 1;
        //do not support GPU in TrialFaceSDK
        Gaiulinya extractor = new Gaiulinya(-1);
        Mat img1 = Imgcodecs.imread("../img/aligned_face1.jpg");
        Mat img2 = Imgcodecs.imread("../img/aligned_face2.jpg");

		float[][] feat1 = extractor.ForwardbyMetaData(encode2bytes(img1), 1, 1);
        float[][] feat2 = extractor.ForwardbyMetaData(encode2bytes(img2), 1, 1);

		System.out.println("The similarity of 2 images is " + Gaiulinya.CosineDistanceProb(feat1[0], feat[1]));
    }
}

Class Description: Gaiulinya

Member Functionvoid Gaiulinya(int device);

Capability: set device(do not support GPU in TrialFaceSDK)

Parameter Parameter Type Value Illustration Remark
device int <0
>=0		 use cpu
use gpu numbered by 'device'
Member Functionfloat[][] ForwardbyMetaData(byte[] data, int faceCount, int order);

Capability: extract 128-dimension feature vector

Parameter Parameter Type Value Illustration Remark
data byte[] user input face image data
num int >0 number of face images
order int array of face image data:NCHW/NHWC order=0(NCHW),otherwise(NHWC)
  • Return Value float[][], 128-dimension face feature vector, outer size of vector is the number of face images, inner size of vector is 128.

Gaiulinya Performance

Test one 3\times 128\times 128(channels \times height \times width) face image on platform i7-8700K, loop 1000 times and calculate average extractTime.

Item Description
Operating System Windows 10 Enterprise 64bit
Processor Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz
Core and Thread 6 Cores and 12 Threads
RAM 32GB
Compiler MSVC 19.5
OpenMP Yes
SIMD Instruction Set AVX2
extractTime(ms) 54.191