Tag detection for preventing unauthorized face image processing

A new technology is being proposed as a solution to the problem of unintentional facial detection and recognition in pictures in which the individuals appearing want to express their privacy preferences, through the use of different tags. The existing methods for face de-identification were mostly ad hoc solutions that only provided an absolute binary solution in a privacy context such as pixelation, or a bar mask. As the number and users of social networks are increasing, our preferences regarding our privacy may become more complex, leaving these absolute binary solutions as something obsolete. The proposed technology overcomes this problem by embedding information in a tag which will be placed close to the face without being disruptive. Through a decoding method the tag will provide the preferences that will be applied to the images in further stages.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.

Rotation Invariant Real-time Face Detection and Recognition System

In this report, a face recognition system that is capable of detecting and recognizing frontal and rotated faces was developed. Two face recognition methods focusing on the aspect of pose invariance are presented and evaluated - the whole face approach and the component-based approach. The main challenge of this project is to develop a system that is able to identify faces under different viewing angles in realtime. The development of such a system will enhance the capability and robustness of current face recognition technology. The whole-face approach recognizes faces by classifying a single feature vector consisting of the gray values of the whole face image. The component-based approach first locates the facial components and extracts them. These components are normalized and combined into a single feature vector for classification. The Support Vector Machine (SVM) is used as the classifier for both approaches. Extensive tests with respect to the robustness against pose changes are performed on a database that includes faces rotated up to about 40 degrees in depth. The component-based approach clearly outperforms the whole-face approach on all tests. Although this approach isproven to be more reliable, it is still too slow for real-time applications. That is the reason why a real-time face recognition system using the whole-face approach is implemented to recognize people in color video sequences.

3D face computational photography using PCA spaces

In this paper, we present a 3D face photography system based on a facial expression training dataset, composed of both facial range images (3D geometry) and facial texture (2D photography). The proposed system allows one to obtain a 3D geometry representation of a given face provided as a 2D photography, which undergoes a series of transformations through the texture and geometry spaces estimated. In the training phase of the system, the facial landmarks are obtained by an active shape model (ASM) extracted from the 2D gray-level photography. Principal components analysis (PCA) is then used to represent the face dataset, thus defining an orthonormal basis of texture and another of geometry. In the reconstruction phase, an input is given by a face image to which the ASM is matched. The extracted facial landmarks and the face image are fed to the PCA basis transform, and a 3D version of the 2D input image is built. Experimental tests using a new dataset of 70 facial expressions belonging to ten subjects as training set show rapid reconstructed 3D faces which maintain spatial coherence similar to the human perception, thus corroborating the efficiency and the applicability of the proposed system.

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

Advanced Techniques for Face Recognition under Challenging Environments

Automatically recognizing faces captured under uncontrolled environments has always been a challenging topic in the past decades. In this work, we investigate cohort score normalization that has been widely used in biometric verification as means to improve the robustness of face recognition under challenging environments. In particular, we introduce cohort score normalization into undersampled face recognition problem. Further, we develop an effective cohort normalization method specifically for the unconstrained face pair matching problem. Extensive experiments conducted on several well known face databases demonstrate the effectiveness of cohort normalization on these challenging scenarios. In addition, to give a proper understanding of cohort behavior, we study the impact of the number and quality of cohort samples on the normalization performance. The experimental results show that bigger cohort set size gives more stable and often better results to a point before the performance saturates. And cohort samples with different quality indeed produce different cohort normalization performance. Recognizing faces gone after alterations is another challenging problem for current face recognition algorithms. Face image alterations can be roughly classified into two categories: unintentional (e.g., geometrics transformations introduced by the acquisition devide) and intentional alterations (e.g., plastic surgery). We study the impact of these alterations on face recognition accuracy. Our results show that state-of-the-art algorithms are able to overcome limited digital alterations but are sensitive to more relevant modifications. Further, we develop two useful descriptors for detecting those alterations which can significantly affect the recognition performance. In the end, we propose to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.

Throne Leg in the form of a Griffin, outer face

Metalwork, Iran, Umayyad Caliphate; 1 ft. 10 7/16 in.x 3 27/64 in.x 7 23/64 in.; cast and chased bronze

Holy Face

Gerard David; 3 35/64 in.x 2 1/8 in.; tempera and gold leaf on parchment that has been trimmed and laid down on thin walnut

Optimization of Gabor Wavelets for Face Recognition

The article describes researches of a method of person recognition by face image based on Gabor wavelets. Scales of Gabor functions are determined at which the maximal percent of recognition for search of a person in a database and minimal percent of mistakes due to false alarm errors when solving an access control task is achieved. The carried out researches have shown a possibility of improvement of recognition system work parameters in the specified two modes when the volume of used data is reduced.

Misaligned and Polarity-Reversed Faces Determine Face-specific Capacity Limits

Previous research using flanker paradigms suggests that peripheral distracter faces are automatically processed when participants have to classify a single central familiar target face. These distracter interference effects disappear when the central task contains additional anonymous (non-target) faces that load the search for the face target, but not when the central task contains additional non-face stimuli, suggesting there are face-specific capacity limits in visual processing. Here we tested whether manipulating the format of non-target faces in the search task affected face-specific capacity limits. Experiment 1 replicated earlier findings that a distracter face is processed even in high load conditions when participants looked for a target name of a famous person among additional names (non-targets) in a central search array. Two further experiments show that when targets and non-targets were faces (instead of names), however, distracter interference was eliminated under high load—adding non-target faces to the search array exhausted processing capacity for peripheral faces. The novel finding was that replacing non-target faces with images that consisted of two horizontally misaligned face-parts reduced distracter processing. Similar results were found when the polarity of a non-target face image was reversed. These results indicate that face-specific capacity limits are not determined by the configural properties of face processing, but by face parts.

Comparative study of human age estimation based on hand-crafted and deep face features

In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

Effects Of Misalignments In The Retinal Nerve Fiber Layer Thickness Measurements With Spectral Domain Optical Coherence Tomography.

Purpose. To investigate misalignments (MAs) on retinal nerve fiber layer thickness (RNFLT) measurements obtained with Cirrus(©) SD-OCT. Methods. This was a retrospective, observational, cross-sectional study. Twenty-seven healthy and 29 glaucomatous eyes of 56 individuals with one normal exam and another showing MA were included. MAs were defined as an improper alignment of vertical vessels in the en face image. MAs were classified in complete MA (CMA) and partial MA (PMA), according to their site: 1 (superior, outside the measurement ring (MR)), 2 (superior, within MR), 3 (inferior, within MR), and 4 (inferior, outside MR). We compared RNFLT measurements of aligned versus misaligned exams in all 4 sectors, in the superior area (sectors 1 + 2), inferior area (sectors 3 + 4), and within the measurement ring (sectors 2 + 3). Results. RNFLT measurements at 12 clock-hour of eyes with MAs in the superior area (sectors 1 + 2) were significantly lower than those obtained in the same eyes without MAs (P = 0.043). No significant difference was found in other areas (sectors 1 + 2 + 3 + 4, sectors 3 + 4, and sectors 2 + 3). Conclusion. SD-OCT scans with superior MAs may present lower superior RNFLT measurements compared to aligned exams.

Padronização de um método para mensuração das tábuas ósseas vestibular e lingual dos maxilares na Tomografia Computadorizada de Feixe Cônico (Cone Beam)

INTRODUÇÃO: a espessura das tábuas ósseas que recobrem os dentes por vestibular e lingual constitui um dos fatores limitantes da movimentação dentária. O avanço tecnológico em Imaginologia permitiu avaliar detalhadamente essas regiões anatômicas por meio da utilização da tomografia computadorizada de feixe cônico (TCFC). OBJETIVO: descrever e padronizar, pormenorizadamente, um método para mensuração das tábuas ósseas vestibular e lingual dos maxilares nas imagens de tomografia computadorizada de feixe cônico. METODOLOGIA: a padronização digital da posição da imagem da face deve constituir o primeiro passo antes da seleção dos cortes de TCFC. Dois cortes axiais de cada maxilar foram empregados para a mensuração da espessura do osso alveolar vestibular e lingual. Utilizou-se como referência a junção cemento-esmalte dos primeiros molares permanentes, tanto na arcada superior quanto na inferior. RESULTADOS: cortes axiais paralelos ao plano palatino foram indicados para avaliação quantitativa do osso alveolar na maxila. Na arcada inferior, os cortes axiais devem ser paralelos ao plano oclusal funcional. CONCLUSÃO: o método descrito apresenta reprodutibilidade para utilização em pesquisas, assim como para a avaliação clínica das repercussões periodontais da movimentação dentária, ao permitir a comparação de imagens pré e pós-tratamento.

Emotional arousal amplifies the effects of biased competition in the brain

The arousal-biased competition model predicts that arousal increases the gain on neural competition between stimuli representations. Thus, the model predicts that arousal simultaneously enhances processing of salient stimuli and impairs processing of relatively less-salient stimuli. We tested this model with a simple dot-probe task. On each trial, participants were simultaneously exposed to one face image as a salient cue stimulus and one place image as a non-salient stimulus. A border around the face cue location further increased its bottom-up saliency. Before these visual stimuli were shown, one of two tones played: one that predicted a shock (increasing arousal) or one that did not. An arousal-by-saliency interaction in category-specific brain regions (fusiform face area for salient faces and parahippocampal place area for non-salient places) indicated that brain activation associated with processing the salient stimulus was enhanced under arousal whereas activation associated with processing the non-salient stimulus was suppressed under arousal. This is the first functional magnetic resonance imaging study to demonstrate that arousal can enhance information processing for prioritized stimuli while simultaneously impairing processing of non-prioritized stimuli. Thus, it goes beyond previous research to show that arousal does not uniformly enhance perceptual processing, but instead does so selectively in ways that optimizes attention to highly salient stimuli.