Palmprint region of interest (ROI) extraction under unconstrained conditions is an unavoidable problem in realizing palmprint recognition. However, the diversity of palm size, posture, illumination, and background undoubtedly poses a great challenge. Therefore, in response to this problem, we collected about 30,000 unconstrained palmprint images from 100 people using five mobile phones with the flash on and off and then manually annotated the 14 key points of each image, which constitutes the most challenging palmprint database at present, namely XJTU-UP. In addition, we propose a method for palmprint ROI extraction. First, palm detection is performed to remove irrelevant background, then detect the key points, and finally establish a coordinate system based on the obtained points to extract the ROI. In the process of key points detection, an auxiliary network and data imbalance functionality are introduced to improve the accuracy. Finally, the experimental results on the XJTU-UP database show that the recognition accuracy has a maximum increase of 2.16% and the true acceptance rate is improved by up to 20.56% when the false acceptance rate is 0.01% compared to suboptimal method.
Recently, palmprint recognition has made huge progress and attracted the attention of more and more researchers. However, current research rarely involves open-set palmprint recognition. We proposed deep ensemble hashing (DEH) for open-set palmprint recognition. Based on the online gradient boosting model, we trained multiple learners in DEH, which focus on identifying different samples. In order to increase the diversity between learners, activation loss and adversarial loss were introduced. Through minimizing activation loss, the neurons of different learners restrained each other, and through adversarial loss, the optimal distance between the features extracted by different learners was obtained. Palmprint identification and verification experiments were performed on PolyU multispectral database and our self-built databases. The results show the effectiveness of DEH in deal with open-set palmprint recognition. Compared to baseline models, DEH increased the recognition accuracy by up to 6.67% and reduced the equal error rate by up to 3.48%.
We present a novel feature extraction method for face recognition called neighborhood discriminant embedding (NDE), which incorporates graph embedding and Fisher's criterion and includes an individual discriminative factor (IDF). Graph embedding is able to reveal the representative and discriminative features from the underlying nonlinear face data structure. Fisher's criterion is recognized as an effective technique for discriminative feature extraction. IDF is proposed as an individual property of each sample to describe the contribution to classification. NDE can remain the local structure of the nearest neighbors of each data point during the dimensionality reduction as well as gather the within-class points and separate the between-class points in the low-dimensional projected space. Utilizing Fisher's criterion and taking into account IDF, the discriminative capability of NDE is further enhanced. Comprehensive experiments are conducted using the Olivetti Research Laboratory (ORL) and Facial Recognition Technology (FERET) face databases to demonstrate the effectiveness of our methods.