Many previous studies on face recognition attempted to seek a precise classifier to achieve
a low misclassification error, which is based on an assumption that all misclassification costs …

Cost-sensitive face recognition is a challenging problem in pattern recognition. Due to the
high-dimensional face features, cost-sensitive face recognition usually conducts feature …

The paper addresses the issue of insufficient speed of image recognition methods if the
number of classes is rather large. We propose the novel algorithm based on sequential …

This paper proposes a novel nonnegative sparse representation approach, called two-stage
sparse representation (TSR), for robust face recognition on a large-scale database. Based …

A limited number of available training samples have become one bottleneck of face
recognition. In real-world applications, the face image might have various changes owing to …

Sparse representation (or coding)-based classification (SRC) has gained great success in
face recognition in recent years. However, SRC emphasizes the sparsity too much and …

Most traditional face recognition systems attempt to achieve a low recognition error rate,
implicitly assuming that the losses of all misclassifications are the same. In this paper, we …

Widespread acceptability and use of biometrics for person authentication has instigated
several techniques for evading identification. One such technique is altering facial …

Existing pruning techniques preserve deep neural networks' overall ability to make correct
predictions but could also amplify hidden biases during the compression process. We …

The image of a face varies with the illumination, pose, and facial expression, thus we say
that a single face image is of high uncertainty for representing the face. In this sense, a face …