3D Model Based Pose Invariant Face Recognition from a Single Frontal View

This paper proposes a 3D model based pose invariant face recognition method that can recognize a face of a large rotation angle from its single nearly frontal view. The proposed method achieves the goal by using an analytic-to-holistic approach and a novel algorithm for estimation of ear points. Firstly, the proposed method achieves facial feature detection, in which an edge map based algorithm is developed to detect the ear points. Based on the detected facial feature points 3D face models are computed and used to achieve pose estimation. Then we reconstruct the facial feature points' locations and synthesize facial feature templates in frontal view using computed face models and estimated poses. Finally, the proposed method achieves face recognition by corresponding template matching and corresponding geometric feature matching. Experimental results show that the proposed face recognition method is robust for pose variations including both seesaw rotations and sidespin rotations

Automatic human face detection for content-based image annotation

In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity

Analytic eye movement patterns in face recognition are associated with better performance and more top-down control of visual attention: an fMRI study

Conference Theme: Integrating Psychological, Philosophical, Linguistic, Computational and Neural PerspectivesPoster Session 3: no. 57Recent research has revealed two different eye movement patterns during face recognition: holistic and analytic. The present study investigated the neural correlates of these two patterns through functional magnetic resonance imaging (fMRI). A more holistic pattern was associated with more activation in the face-selective perceptual areas, including the occipital face area and fusiform face area. In contrast, participants using a more analytic pattern demonstrated more activation in areas important for top-down control of visual attention, including the frontal eye field and intraparietal sulcus. In addition, participants using the analytic patterns had better recognition performance than those showing holistic patterns. These results suggest that analytic eye movement patterns are associated with more engagement of top-down control of visual attention, which may consequently enhance recognition performance.postprin

Machine Analysis of Facial Expressions

No abstract

Perception of global facial geometry is modulated through experience

Identification of personally familiar faces is highly efficient across various viewing conditions. While the presence of robust facial representations stored in memory is considered to aid this process, the mechanisms underlying invariant identification remain unclear. Two experiments tested the hypothesis that facial representations stored in memory are associated with differential perceptual processing of the overall facial geometry. Subjects who were personally familiar or unfamiliar with the identities presented discriminated between stimuli whose overall facial geometry had been manipulated to maintain or alter the original facial configuration (see Barton, Zhao & Keenan, 2003). The results demonstrate that familiarity gives rise to more efficient processing of global facial geometry, and are interpreted in terms of increased holistic processing of facial information that is maintained across viewing distances

Eye movement pattern in face recognition is associated with cognitive decline in the elderly

Conference Theme: Mind, Technology, and SocietyThe present study investigated the relationship between eye movement pattern in face recognition and cognitive perform-ance during natural aging through modeling and comparing eye movement of young (18-24 years) and older (65-81 years) adults using Hidden Markov Model (HMM) based approach. Young adults recognized faces better than older adults, particularly when measured by the false alarm rate. Older adults’ recognition performance, on the other hand, correlated with their cognitive status assessed by the Montreal Cognitive Assessment (MoCA). Eye movement analysis with HMM revealed two different strategies, namely “analytic” and “holistic”. Participants using the analytic strategy had better recognition performance (particularly in the false alarm rate) than those using the holistic strategy. Significantly more young adults adopted the analytic strategy; whereas more older adults adopted the holistic strategy. Interestingly, older adults with lower cognitive status were associated with higher likelihood of using the holistic strategy. These results suggest an association between holistic eye movement patterns and cognitive decline in the elderly.postprin

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2