Method and System for Face Recognition Via Deep Learning

A deep learning-based face identification method comprising:

• acquiring an aligned face image;
• scaling the face image at a preset ratio to obtain a target image;
• extracting a pixel matrix of the target image;
• inputting the pixel matrix to a neural network model;
• and executing multilayer computing to obtain a computing result;
• and
• calculating a cosine similarity degree between the computing result and a facial template vector, wherein comparison is successful if the cosine similarity degree is greater than a preset threshold.

An electronic apparatus for deep learning-based face identification, the electronic apparatus comprising:

• an acquisition unit configured to:
• acquire an aligned face image, and
• scale the face image at a preset ratio to obtain a target image;
• an extraction unit configured to:
• extract a pixel matrix of the target image, and
• input the pixel matrix to a preset trained neural network model, and execute multilayer forward computing to obtain a computing result;
• and
• a calculation unit configured to calculate a cosine similarity degree between the computing result and a facial template vector, wherein comparison is successful if a matching value is greater than the cosine similarity degree.

A non-transitory machine-readable medium having information recorded thereon for performing deep learning-based face identification, wherein the information, when read by the machine, causes the machine to perform the following:

• acquiring an aligned face image;
• scaling the face image at a preset ratio to obtain a target image;
• extracting a pixel matrix of the target image;
• inputting the pixel matrix to a neural network model;
• and executing multilayer computing to obtain a computing result;
• and
• calculating a cosine similarity degree between the computing result and a facial template vector, wherein comparison is successful if the cosine similarity degree is greater than a preset threshold.

An apparatus for face identification comprising:

• a memory;
• and
• a processor configured to implement a neural network model including:
• a first convolutional layer,
• a second convolutional layer,
• a third convolutional layer,
• a first bottleneck network,
• a second bottleneck network,
• a third bottleneck network,
• a fourth bottleneck network,
• a first pooling layer,
• a second pooling layer,
• a third pooling layer, and
• a fourth pooling layer, wherein
• the first convolutional layer, the first bottleneck network, the first pooling layer, the second bottleneck network, the second convolutional layer, the second pooling layer, the third bottleneck network, the third convolutional layer, the third pooling layer, the fourth bottleneck network and the fourth pooling layer are connected in series sequentially, and wherein
• the first convolutional layer, the first bottleneck network, the first pooling layer, the second bottleneck network, the second convolutional layer, the second pooling layer, the third bottleneck network, the third convolutional layer, the third pooling layer, the fourth bottleneck network and the fourth pooling layer are densely connected according to a connection policy.