Recent face composite and synthesis related works have shown promising results in generating realistic face images from deep convolutional networks. However, these works either do not generate consistent results when the constituent patches contain large domain variations (i.e., from face and sketch domains) or cannot generate high-resolution images with limited facial patches (e.g., the inpain...

Characteristic problems with social interaction have prompted considerable interest in the face processing of individuals with Autism Spectrum Disorder (ASD). Studies suggest that reduced integration of information from disparate facial regions likely contributes to difficulties recognizing static faces in this population. Recent work also indicates that observers with ASD have problems using p...

With the wide applications of user authentication based on face recognition, face spoof attacks against face recognition systems are drawing increasing attentions. While emerging approaches of face antispoofing have been reported in recent years, most of them limit to the non-realistic intra-database testing scenarios instead of the crossdatabase testing scenarios. We propose a robust represent...

A new algorithm is developed in this paper to support automatic name-face alignment for achieving more accurate cross-media news retrieval. We focus on extracting valuable information from large amounts of news images and their captions, where multi-level image-caption pairs are constructed for characterizing both significant names with higher salience and their cohesion with human faces extrac...

Recognition for human faces, monkey faces, and objects was assessed in both adult humans (Homo sapiens) and monkeys (Macaca mulatta) with a visual paired-comparison task. The results demonstrated that while both species showed strong novelty preference for objects, human participants showed novelty preference for human faces but not for monkey faces, and vice versa for the monkeys. This `specie...

In this paper, we present a deep coupled learning framework to address the problem of matching polarimetric thermal face photos against a gallery of visible faces. Polarization state information of thermal faces provides the missing textural and geometrics details in the thermal face imagery which exist in visible spectrum. we propose a coupled deep neural network architecture which leverages r...