Research And Implementation Of Bimodal Face Recognition System

In the past two decades face recognition has attracted much attention. People hope that the computer can have a powerful capability of using the face to recognize the identity as we can do and the world can become more'intelligent'. However, up to now, automatic face recognition does not reach this goal of people. Especially under the complex condition, the performance of conventional face recognition technique that is constructed on the basis of the computation of the similarity between the face images cannot meet the requirement of real-world applications. For example, when the face is partially occluded, conventional face recognition technique usually incorrectly recognizes or rejects to recognize the face. The research also shows that under the condition of greatly varying lighting condition, the difference of the images of the same face obtained using conventional face recognition methods might be greater than the difference of two images respectively generated from two different faces. Consequently, this will lead to incorrect recognition result. Indeed this factor is also one of the reasons why the automatic face recognition technique is not as popular as the fingerprint recognition technique.After the sparse representation method is applied to face recognition, a breakthrough has been made. Previous experiments show that under many cases including the case where the face is partially occluded or there is noise, the sparse method can obtain much higher classification accuracy than other methods. Indeed, sparse-representation-based face recognition technique is a novel methodology and it provides us a novel idea and viewpoint that is very useful for addressing the technical problems in face recognition. However, when the sparse representation method is applied to image recognition, there are still some unsolved problems. For example, previous sparse representation methods exploit only a portion of the whole training image set to represent the test image, but it is not known which training images are exploited and which are not. Moreover, because the dimension of the face image is very great, the computational efficiency of the sparse representation methods will be very low especially in the case where the training sample set has a large size. This is disadvantageous for the real-world applications. In order to solve the above issues, we devise several novel sparse-representation-idea-based face recognition methods. These methods not only are computationally efficient but also can obtain high accuracies. To address the issue that the performance of previous visible-light-based face recognition systems is affected by the varying illumination, we devise a face recognition system that combines visible light images and infrared red images of the face to robustly classify the face. This system has an excellent performance.