Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time

MODELING AND ANALYSIS OF WRINKLES ON AGING HUMAN FACES

The analysis and modeling of aging human faces has been extensively studied in the past decade. Most of this work is based on matching learning techniques focused on appearance of faces at different ages incorporating facial features such as face shape/geometry and patch-based texture features. However, we do not find much work done on the analysis of facial wrinkles in general and specific to a person. The goal of this dissertation is to analyse and model facial wrinkles for different applications. Facial wrinkles are challenging low-level image features to analyse. In general, skin texture has drastically varying appearance due to its characteristic physical properties. A skin patch looks very different when viewed or illuminated from different angles. This makes subtle skin features like facial wrinkles difficult to be detected in images acquired in uncontrolled imaging settings. In this dissertation, we examine the image properties of wrinkles i.e. intensity gradients and geometric properties and use them for several applications including low-level image processing for automatic detection/localization of wrinkles, soft biometrics and removal of wrinkles using digital inpainting. First, we present results of detection/localization of wrinkles in images using Marked Point Process (MPP). Wrinkles are modeled as sequences of line segments in a Bayesian framework which incorporates a prior probability model based on the likely geometric properties of wrinkles and a data likelihood term based on image intensity gradients. Wrinkles are localized by sampling the posterior probability using a Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm. We also present an evaluation algorithm to quantitatively evaluate the detection and false alarm rate of our algorithm and conduct experiments with images taken in uncontrolled settings. The MPP model, despite its promising localization results, requires a large number of iterations in the RJMCMC algorithm to reach global minimum resulting in considerable computation time. This motivated us to adopt a deterministic approach based on image morphology for fast localization of facial wrinkles. We propose image features based on Gabor filter banks to highlight subtle curvilinear discontinuities in skin texture caused by wrinkles. Then, image morphology is used to incorporate geometric constraints to localize curvilinear shapes of wrinkles at image sites of large Gabor filter responses. We conduct experiments on two sets of low and high resolution images to demonstrate faster and visually better localization results as compared to those obtained by MPP modeling. As a next application, we investigate the user-drawn and automatically detected wrinkles as a pattern for their discriminative power as a soft biometrics to recognize subjects from their wrinkle patterns only. A set of facial wrinkles from an image is treated as a curve pattern and used for subject recognition. Given the wrinkle patterns from a query and gallery images, several distance measures are calculated between the two patterns to quantify the similarity between them. This is done by finding the possible correspondences between curves from the two patterns using a simple bipartite graph matching algorithm. Then several metrics are used to calculate the similarity between the two wrinkle patterns. These metrics are based on Hausdorff distance and curve-to-curve correspondences. We conduct experiments on data sets of both hand drawn and automatically detected wrinkles. Finally, we apply digital inpainting to automatically remove wrinkles from facial images. Digital image inpainting refers to filling in the holes of arbitrary shapes in images so that they seem to be part of the original image. The inpainting methods target either the structure or the texture of an image or both. There are two limitations of existing inpainting methods for the removal of wrinkles. First, the differences in the attributes of structure and texture requires different inpainting methods. Facial wrinkles do not fall strictly under the category of structure or texture and can be considered as some where in between. Second, almost all of the image inpainting techniques are supervised i.e. the area/gap to be filled is provided by user interaction and the algorithms attempt to find the suitable image portion automatically. We present an unsupervised image inpainting method where facial regions with wrinkles are detected automatically using their characteristic intensity gradients and removed by painting the regions by the surrounding skin texture

Evaluation of different chrominance models in the detection and reconstruction of faces and hands using the growing neural gas network

Physical traits such as the shape of the hand and face can be used for human recognition and identification in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems suffers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates different statistical and chrominance models in different environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the filtering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling’s, Alicante, Essex, and Stegmann’s) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under different environmental conditions

Bio : A Mulrimodal biometric authentication system for person identification and verification

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Placing faces: recollection and familiarity in the own-race bias for face recognition

The research presented in this thesis examined the roles of recollection and familiarity in the own-race bias (ORB) in recognition memory for faces. In Paper 1, Jacoby’s (1991) process-dissociation procedure was used to estimate the relative contributions of recollection and familiarity in recognizing own- and other-race faces. Recollection estimates were higher for own-race faces than for other-race faces, although this effect disappeared when deep or shallow encoding strategies were encouraged. In Paper 2, participants were shown to be less accurate at ignoring previously seen other-race distractors than own-race distractors. Papers 3 and 4 examined how accurately participants were able to remember contextual information about correctly recognized faces. In the encoding phase of an old/new recognition test, each target face was paired with one of several different backgrounds. At testing, old judgments were followed by context judgments, in which the participant attempted to identify with which background the face had been paired. The context judgments were consistently more accurate for correctly recognized own-race faces than for correctly recognized other-race faces. This effect was robust to experimental manipulations such as context reinstatement and divided attention. The overall conclusion from this thesis is that recollection is inferior for other-race faces compared to own-race faces. This recollection deficit means that it is more difficult to retrieve specific information about the circumstances in which other-race faces were encountered. The implications of this recollection deficit for real world behaviour are discussed, with particular reference to eyewitness memory

Episodic memory for emotional information: Event-related potential and functional magnetic resonance imaging studies

The neural correlates of emotional episodic memory are investigated in a series of neuroimaging experiments (ERP, fMRI) through the comparison of memory effects elicited during retrieval of emotional relative to neutral information. In the first two ERP studies, it is revealed that emotionally-valenced words influence recognition memory primarily by virtue of their high levels of 'semantic-cohesiveness'. Furthermore, the findings reveal that the arrangement of emotional and neutral retrieval cues at test (blocked versus intermixed) influences processing carried out upon retrieved emotional episodic information. The findings across the third and fourth ERP studies indicate that incidental retrieval of emotional context (encoding environment) gives rise to greater activity in neural systems supporting episodic retrieval than does retrieval of non-emotional context. When context retrieval is intentional, by contrast, emotional and non-emotional episodic memory are associated with equivalent levels of engagement. The findings of the fourth ERP study are consistent with the existence of additional neural circuitry that is activated selectively by emotionally toned episodic information. In a final event-related fMRI study it is revealed that the retrieval of emotionally negative relative to emotionally neutral context elicits enhanced activity in brain regions including prefrontal cortex, amygdala, hippocampus and retrosplenial cortex. Recognition of words from positive relative to neutral contexts is associated with increased activity in prefrontal and orbitofrontal cortex, and in the left anterior temporal lobe. The fMRI findings provide further support for the proposal that the incidental retrieval of emotional information enhances activity in networks supporting episodic retrieval of neutral information. In addition, the fMRI findings suggest that regions known to be activated when emotional information is encountered in the environment are also active when emotional information is retrieved from memory. Whilst the findings are noteworthy in their own right, they also have implications for future studies of emotional memory. It is proposed that the employment of paradigms which involve the retrieval of emotional context through presentation of non-emotional retrieval cues may offer advantages over paradigms wherein the retrieval cues themselves are emotional

Interpersonal and Ideological Kindness: A Biocultural Approach

In accordance with Richard Dawkins’ materialist “selfish gene” theory of human behavior, altruism is a subject matter that is treated conservatively by biologists, whose understanding of the human version of altruism tends toward mutualistic and sometimes reputation-based explanations of charity, kindness, and helping. Trivers (1971) first stated that non-kin altruism could evolve if altruistic behavior is balanced between partners over time, implicating a strictly mutualistic domain for kindness. But kindness herein is defined, beyond mere mutualism or reciprocity, as “the quality of being friendly, generous, and considerate.” Further, kindness tends to have an action-oriented dimension, as in Goetz et al.’s (2010) definition of compassion, denoting helpfulness, the reduction of another’s suffering, or self-sacrifice. In this paper, I will employ a biocultural approach in exploring the psychological and neuroscientific data on the evolutionary aspect of social behavior as it pertains to kindness. First, I will draw on evolutionary theories of cooperation in suggesting that an individual and ideological ethos of kindness could have evolved as an adaptive orientation that, in a Durkheimian sense, preempted ostracism and cemented alliances as a beneficial balance to the fitness risks inherent in altruism. Then, consulting data on the neurochemical profiles of dopamine and oxytocin, I will describe the sort of human psychological variation that would reveal a complimentary continuum of evolved social proclivities, from selfish to giving. In proposing that non-reciprocal kindness indeed exists, however, I argue that its presence in human societies is statistically rare, as assumptions about human biology suggest. This study thus concludes with a cautious message about the human condition: while the rareness of kindness should have a profoundly fundamental explanatory value in social analysis, scientific confirmation of its fragility would recommend further scholarship designed to highlight its exceptional biological position vis-à-vis the selfish gene

Monkeys and Humans Share a Common Computation for Face/Voice Integration

Speech production involves the movement of the mouth and other regions of the face resulting in visual motion cues. These visual cues enhance intelligibility and detection of auditory speech. As such, face-to-face speech is fundamentally a multisensory phenomenon. If speech is fundamentally multisensory, it should be reflected in the evolution of vocal communication: similar behavioral effects should be observed in other primates. Old World monkeys share with humans vocal production biomechanics and communicate face-to-face with vocalizations. It is unknown, however, if they, too, combine faces and voices to enhance their perception of vocalizations. We show that they do: monkeys combine faces and voices in noisy environments to enhance their detection of vocalizations. Their behavior parallels that of humans performing an identical task. We explored what common computational mechanism(s) could explain the pattern of results we observed across species. Standard explanations or models such as the principle of inverse effectiveness and a “race” model failed to account for their behavior patterns. Conversely, a “superposition model”, positing the linear summation of activity patterns in response to visual and auditory components of vocalizations, served as a straightforward but powerful explanatory mechanism for the observed behaviors in both species. As such, it represents a putative homologous mechanism for integrating faces and voices across primates

How expertise and motivation affect the recognition of own- and other-race faces: Behavioural and electrophysiological evidence

Humans have difficulties recognising other-race faces, and this own-race bias (ORB) has been explained in terms of either reduced perceptual expertise with other-race faces or socio-cognitive and motivational factors, such as categorisation of other-race faces into social out-groups. The aim of this thesis was to investigate the role of these factors to the ORB using behavioural and event-related brain potential (ERP) measures. First, it was investigated whether increasing motivation to individuate other-race faces can reduce or even eliminate the ORB in recognition memory. Chapter 2 revealed that a modulation of face memory by motivational factors is possible, but restricted to face categories for which participants have acquired expertise. In Chapter 3, instructions to individuate and closely attend to other-race faces during learning reduced the ORB, but ERPs recorded during encoding indicated that additional effort was required to overcome difficulties associated with other-race face recognition. Second, it was examined whether own- and other-race faces are learnt equally well from highly variable images in paradigms that encourage individuation of own- and other-race identities. Chapter 4 revealed better learning for own- relative to other-race identities, and only extensive other-race contact eliminated this own-race advantage. In Chapter 5, ERP results indicated that the own-race advantage in identity learning resulted from facilitated processing of own-race faces at an early perceptual level. In sum, the present research suggests that the ORB is mainly driven by differential perceptual expertise. However, motivational factors can modulate the effect when participants have acquired sufficient expertise with a given face category and thus the present results offer novel insights into how expertise and motivation interact