Abstract
Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this problem, this paper proposes a scheme combined traditional and deep learning (TDL) method to process the task. First, it proposes an expanding sample method based on traditional approach. Compared with other expanding sample methods, the method can be used easily and conveniently. Besides, it can generate samples such as disguise, expression, and mixed variation. Second, it uses transfer learning and introduces a well-trained deep convolutional neural network (DCNN) model and then selects some expanding samples to fine-tune the DCNN model. Third, the fine-tuned model is used to implement experiment. Experimental results on AR face database, Extend Yale B face database, FERET face database, and LFW database demonstrate that TDL achieves the state-of-the-art performance in SSPP FR.
Highlights
As artificial intelligence (AI) becomes more and more popular, computer vision (CV) has been proved to be a very hot topic in academic such as face recognition [1], facial expression recognition [2], and object recognition [3]
We find that recognition rates on AR face database are very high which is because the intraclass variation is learned from the same database, recognition rate on LFW database is the lowest among these database which is because the assumption of the model is to deal with frontal faces, so the final system is only working with frontal faces, when it is tested on LFW database which concludes nonfrontal faces the recognition rate dropped sharply
We propose a scheme combined traditional and deep learning (DL) (TDL) method for single sample per person (SSPP) face recognition (FR)
Summary
As artificial intelligence (AI) becomes more and more popular, computer vision (CV) has been proved to be a very hot topic in academic such as face recognition [1], facial expression recognition [2], and object recognition [3]. Speaking, these methods can be summarized in five basic methods: direct method, generic learning method, patchbased method, expanding sample method, and deep learning (DL) method. An expanding sample method is proposed to increase the sample to overcome the shortage of sample in SSPP FR. A learned DCNN model is brought in, and some expanding samples are selected to fine-tune the model. The fine-tuned model is used to perform experiment. (i) We propose a novel expanding sample method. We propose bringing transfer learning into SSPP FR to avoid the requirement of training DCNN that needs abundant samples. We select images from expanding samples to fine-tune the DCNN model. Session 3 presents the expanding sample method.
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