Face in profile view reduces perceived facial expression intensity: an eye-tracking study

Recent studies measuring the facial expressions of emotion have focused primarily on the perception of frontal face images. As we frequently encounter expressive faces from different viewing angles, having a mechanism which allows invariant expression perception would be advantageous to our social interactions. Although a couple of studies have indicated comparable expression categorization accuracy across viewpoints, it is unknown how perceived expression intensity and associated gaze behaviour change across viewing angles. Differences could arise because diagnostic cues from local facial features for decoding expressions could vary with viewpoints. Here we manipulated orientation of faces (frontal, mid-profile, and profile view) displaying six common facial expressions of emotion, and measured participants' expression categorization accuracy, perceived expression intensity and associated gaze patterns. In comparisonwith frontal faces, profile faces slightly reduced identification rates for disgust and sad expressions, but significantly decreased perceived intensity for all tested expressions. Although quantitatively viewpoint had expression-specific influence on the proportion of fixations directed at local facial features, the qualitative gaze distribution within facial features (e.g., the eyes tended to attract the highest proportion of fixations, followed by the nose and then the mouth region) was independent of viewpoint and expression type. Our results suggest that the viewpoint-invariant facial expression processing is categorical perception, which could be linked to a viewpoint-invariant holistic gaze strategy for extracting expressive facial cues

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Side-View Face Recognition

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition