Publications

Abstract : Madarkar, JitendraSharma, PoonamSingh, RimjhimFace recognition accuracy is vulnerable to environmental noise, low-resolution images, and other variations such as illumination, pose, and expression. The accuracy of the face recognition mostly relying on the features of training samples and testing samples. Recently, sparse representation based classification (SRC) has shown state-of-the-art results in face recognition and developed several extended versions of SRC methods to improve the performance. The time complexity of the SRC is depended on the size of the dictionary. In this paper, a new fusion approach MRSRC (Multi-resolution sparse representation based classification) is developed by incorporating the wavelet compressed features into the dictionary. MRSRC has shown better performance than an existing algorithm and also reduces the time complexity. The experimentation is carried out on benchmarking databases such as LFW and ORL.