A Novel Sparse Representation Method Based On Virtual Samples For Face Detection And Recognition System

Recently, face recognition technology has developed rapidly and made a greatprogress, and it has become a hot research in both fields of computer vision andpattern recognition. Because face recognition can realize the authenticationconveniently and directly, and it can be public accept easily, it is worth to beresearched. So far, a lot of face recognition methods have been raised and some ofthem perform very well. However, the automatic face recognition process can beinterfered by a variety of factors such as lighting conditions, posture, facialexpressions, and shelter materials and so on. So how to overcome the impact of thesedisturbances is a big challenge for face recognition. Then, the face recognition systemmay not be able to recognize faces accurately and the authentication work may be notso successfully if the posture has changed a lot or it is in the naturalconditions.Therefore, the direction of the development of face recognition is how toimprove the recognition rate and speed up the recognition process, and improve therobustness, which is also the original intention of our paper.To improve the performance of face recognition, a novel sparse representationmethod based on virtual samples is proposed in this paper, which has two phases,creating virtual samples and representing the testing sample with sparserepresentation algorithm. The first phase of this method is to produce virtual samplesby adding the random noise to original training samples, and exploit both originaltraining samples and virtual samples to obtain the new training set. The second phaseis to represent the testing sample as a linear combination of all the samples in the newtraining set and use the representation result to perform the classification. Due to thismethod can reduce the bad influence caused by the insufficient training samples andthe various facial expressions, the classification accuracy is improved. A number offace recognition experiments show that our method can perform well.During the face recognition process, it’s found that the face detection is the basisof the face recognition, and it is the critical step. In some practical applications, it isnot necessary to accurately identify each specific face of the image or video, but onlyto know the number of faces of the image, such as attendance inspection system.Video-based classroom teaching and examination personnel identification system isjust a such system, which checks the rate of the attending class by automatic facedetection and manual checks, and the final result of the detection will be printed in the excel report format. This system is not only clear interface and easy to operate,but also very user-friendly. It is a good attendance inspection system.