The ENCARA system for face detection and normalization

[EN]Facial image processing is becoming widespread in human-computer applications, despite its complexity. High-level processes such as face recognition or gender determination rely on low-level routines that must e ectively detect and normalize the faces that appear in the input image. In this paper, a face detection and normalization system is described. The approach taken is based on a cascade of fast, weak classi ers that together try to determine whether a frontal face is present in the image.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

A qualitative impairment in face perception in Alzheimer’s disease : evidence from a reduced face inversion effect

Prevalent face recognition difficulties in Alzheimer’s disease (AD) have typically been attributed to the underlying episodic and semantic memory impairment. The aim of the current study was to determine if AD patients are also impaired at the perceptual level for faces, more specifically at extracting a visual representation of an individual face. To address this question, we investigated the matching of simultaneously presented individual faces and of other nonface familiar shapes (cars), at both upright and inverted orientation, in a group of mild AD patients and in a group of healthy older controls matched for age and education. AD patients showed a reduced inversion effect (i.e., larger performance for upright than inverted stimuli) for faces, but not for cars, both in terms of error rates and response times. While healthy participants showed a much larger decrease in performance for faces than for cars with inversion, the inversion effect did not differ significantly for faces and cars in AD. This abnormal inversion effect for faces was observed in a large subset of individual patients with AD. These results suggest that AD patients have deficits in higher-level visual processes, more specifically at perceiving individual faces, a function that relies on holistic representations specific to upright face stimuli. These deficits, combined with their memory impairment, may contribute to the difficulties in recognizing familiar people that are often reported in patients suffering from the disease and by their caregivers.

Miglioramento della qualità di algoritmi di face morphing generation

L'obiettivo principale di questo lavoro di tesi è quello di migliorare gli algoritmi di morphing generation in termini di qualità visiva e di potenzialità di attacco dei sistemi automatici di riconoscimento facciale.

Amygdala hyperactivation in untreated depressed individuals

The amygdala participates in the detection and control of affective states, and has been proposed to be a site of dysfunction in affective disorders. To assess amygdala processing in individuals with unipolar depression, we applied a functional MRI (fMRI) paradigm previously shown to be sensitive to amygdala function. Fourteen individuals with untreated DSM-IV major depression and 15 healthy subjects were studied using fMRI with a standardized emotion face recognition task. Voxel-level data sets were subjected to a multiple-regression analysis, and functionally defined regions of interest (ROI), including bilateral amygdala, were analyzed with MANOVA. Pearson correlation coefficients between amygdala activation and HAM-D score also were performed. While both depressed and healthy groups showed increased amygdala activity when viewing emotive faces compared to geometric shapes, patients with unipolar depression showed relatively more activity than healthy subjects, particularly on the left. Positive Pearson correlations between amygdala activation and HAM-D score were found for both left and right ROIs in the patient group. This study provides in vivo imaging evidence to support the hypothesis of abnormal amygdala functioning in depressed individuals. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

Interface caligráfica de escrita no ar

Na atualidade, está a emergir um novo paradigma de interação, designado por Natural User Interface (NUI) para reconhecimento de gestos produzidos com o corpo do utilizador. O dispositivo de interação Microsoft Kinect foi inicialmente concebido para controlo de videojogos, para a consola Xbox360. Este dispositivo demonstra ser uma aposta viável para explorar outras áreas, como a do apoio ao processo de ensino e de aprendizagem para crianças do ensino básico. O protótipo desenvolvido visa definir um modo de interação baseado no desenho de letras no ar, e realizar a interpretação dos símbolos desenhados, usando os reconhecedores de padrões Kernel Discriminant Analysis (KDA), Support Vector Machines (SVM) e $N. O desenvolvimento deste projeto baseou-se no estudo dos diferentes dispositivos NUI disponíveis no mercado, bibliotecas de desenvolvimento NUI para este tipo de dispositivos e algoritmos de reconhecimento de padrões. Com base nos dois elementos iniciais, foi possível obter uma visão mais concreta de qual o hardware e software disponíveis indicados à persecução do objetivo pretendido. O reconhecimento de padrões constitui um tema bastante extenso e complexo, de modo que foi necessária a seleção de um conjunto limitado deste tipo de algoritmos, realizando os respetivos testes por forma a determinar qual o que melhor se adequava ao objetivo pretendido. Aplicando as mesmas condições aos três algoritmos de reconhecimento de padrões permitiu avaliar as suas capacidades e determinar o $N como o que apresentou maior eficácia no reconhecimento. Por último, tentou-se averiguar a viabilidade do protótipo desenvolvido, tendo sido testado num universo de elementos de duas faixas etárias para determinar a capacidade de adaptação e aprendizagem destes dois grupos. Neste estudo, constatou-se um melhor desempenho inicial ao modo de interação do grupo de idade mais avançada. Contudo, o grupo mais jovem foi revelando uma evolutiva capacidade de adaptação a este modo de interação melhorando progressivamente os resultados.

Dados de reconhecimento facial em Business Intelligence

Vivemos cada vez mais numa era de crescentes avanços tecnológicos em diversas áreas. O que há uns anos atrás era considerado como praticamente impossível, em muitos dos casos, já se tornou realidade. Todos usamos tecnologias como, por exemplo, a Internet, Smartphones e GPSs de uma forma natural. Esta proliferação da tecnologia permitiu tanto ao cidadão comum como a organizações a sua utilização de uma forma cada vez mais criativa e simples de utilizar. Além disso, a cada dia que passa surgem novos negócios e startups, o que demonstra o dinamismo que este crescimento veio trazer para a indústria. A presente dissertação incide sobre duas áreas em forte crescimento: Reconhecimento Facial e Business Intelligence (BI), assim como a respetiva combinação das duas com o objetivo de ser criado um novo módulo para um produto já existente. Tratando-se de duas áreas distintas, é primeiramente feito um estudo sobre cada uma delas. A área de Business Intelligence é vocacionada para organizações e trata da recolha de informação sobre o negócio de determinada empresa, seguindo-se de uma posterior análise. A grande finalidade da área de Business Intelligence é servir como forma de apoio ao processo de tomada de decisão por parte dos analistas e gestores destas organizações. O Reconhecimento Facial, por sua vez, encontra-se mais presente na sociedade. Tendo surgido no passado através da ficção científica, cada vez mais empresas implementam esta tecnologia que tem evoluído ao longo dos anos, chegando mesmo a ser usada pelo consumidor final, como por exemplo em Smartphones. As suas aplicações são, portanto, bastante diversas, desde soluções de segurança até simples entretenimento. Para estas duas áreas será assim feito um estudo com base numa pesquisa de publicações de autores da respetiva área. Desde os cenários de utilização, até aspetos mais específicos de cada uma destas áreas, será assim transmitido este conhecimento para o leitor, o que permitirá uma maior compreensão por parte deste nos aspetos relativos ao desenvolvimento da solução. Com o estudo destas duas áreas efetuado, é então feita uma contextualização do problema em relação à área de atuação da empresa e quais as abordagens possíveis. É também descrito todo o processo de análise e conceção, assim como o próprio desenvolvimento numa vertente mais técnica da solução implementada. Por fim, são apresentados alguns exemplos de resultados obtidos já após a implementação da solução.

Disseny i implementació hardware d'algorismes de reconeixement de caràcters per comptadors d'aigua

Aquest projecte consisteix en l'estudi, comparació i implementació en hardware d'algoritmes de reconeixement de caràcters per integrar en un sistema intel·ligent de captura d'imatges. Aquest sistema, integrat per una càmera amb format i característiques específiques i que anirà acoblat a un comptador d'aigua tradicional, en captarà imatges i les enviarà per RF al punt de recepció de la companyia. L'objectiu principal consisteix en aconseguir un disseny que redueixi al màxim la quantitat d'informació per transmetre, tenint en compte les limitacions de l'entorn.

Eigen Faces: Detecció, Classificació/Reconeixement de cares

En aquest projecte es pretén utilitzar mètodes coneguts com ara Viola&Jones (detecció) i EigenFaces (reconeixement) per a detectar i reconèixer cares dintre d’imatges de vídeo. Per a aconseguir aquesta tasca cal partir d’un conjunt de dades d’entrenament per a cada un dels mètodes (base de dades formada per imatges i anotacions manuals). A partir d’aquí, l’aplicació, ha de ser capaç de detectar cares en noves imatges i reconèixer-les (identificar de quina cara es tracta)

Identificació de subjectes a partir de l'anàlisi de dades multimodals RGB-Depth

En aquest treball s'explora el camp de la identificació facial de subjectes utilitzant tècniques d'anàlisi multimodal. Això és utilitzant imatges RGB i imatges de profunditat (3D) amb l'objecte de validar les diverses tècniques emprades en el reconeixement facial i aprofundir en sistemes que incorporen informació tridimensional als algorismes de detecció i identificació facial.

The relative salience of facial features when differentiating faces based on an interference paradigm

Research on face recognition and social judgment usually addresses the manipulation of facial features (eyes, nose, mouth, etc.). Using a procedure based on a Stroop-like task, Montepare and Opeyo (J Nonverbal Behav 26(1):43-59, 2002) established a hierarchy of the relative salience of cues based on facial attributes when differentiating faces. Using the same perceptual interference task, we established a hierarchy of facial features. Twenty-three participants (13 men and 10 women) volunteered for the experiment to compare pairs of frontal faces. The participants had to judge if the eyes, nose, mouth and chin in the pair of images were the same or different. The factors manipulated were the target-distractive factor (4 face components 9 3 distractive factors), interference (absent vs. present) and correct answer (the same vs. different). The analysis of reaction times and errors showed that the eyes and mouth were processed before the chin and nose, thus highlighting the critical importance of the eyes and mouth, as shown by previous research.

Animal modelling for inherited central vision loss.

Disease-causing variants of a large number of genes trigger inherited retinal degeneration leading to photoreceptor loss. Because cones are essential for daylight and central vision such as reading, mobility, and face recognition, this review focuses on a variety of animal models for cone diseases. The pertinence of using these models to reveal genotype/phenotype correlations and to evaluate new therapeutic strategies is discussed. Interestingly, several large animal models recapitulate human diseases and can serve as a strong base from which to study the biology of disease and to assess the scale-up of new therapies. Examples of innovative approaches will be presented such as lentiviral-based transgenesis in pigs and adeno-associated virus (AAV)-gene transfer into the monkey eye to investigate the neural circuitry plasticity of the visual system. The models reported herein permit the exploration of common mechanisms that exist between different species and the identification and highlighting of pathways that may be specific to primates, including humans.

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

An Investigation of the Role of Attention in the Cross-Race Effect: An Ecological Approach

The current set of studies was conducted to examine the cross-race effect (CRE), a phenomenon commonly found in the face perception literature. The CRE is evident when participants display better own-race face recognition accuracy than other-race recognition accuracy (e.g. Ackerman et al., 2006). Typically the cross-race effect is attributed to perceptual expertise, (i.e., other-race faces are processed less holistically; Michel, Rossion, Han, Chung & Caldara, 2006), and the social cognitive model (i.e., other-race faces are processed at the categorical level by virtue of being an out-group member; Hugenberg, Young, Bernstein, & Sacco, 2010). These effects may be mediated by differential attention. I investigated whether other-race faces are disregarded and, consequently, not remembered as accurately as own-race (in-group) faces. In Experiment 1, I examined how the magnitude of the CRE differed when participants learned individual faces sequentially versus when they learned multiple faces simultaneously in arrays comprising faces and objects. I also examined how the CRE differed when participants recognized individual faces presented sequentially versus in arrays of eight faces. Participants’ recognition accuracy was better for own-race faces than other-race faces regardless of familiarization method. However, the difference between own- and other-race accuracy was larger when faces were familiarized sequentially in comparison to familiarization with arrays. Participants’ response patterns during testing differed depending on the combination of familiarization and testing method. Participants had more false alarms for other-race faces than own-race faces if they learned faces sequentially (regardless of testing strategy); if participants learned faces in arrays, they had more false alarms for other-race faces than own-races faces if ii i they were tested with sequentially presented faces. These results are consistent with the perceptual expertise model in that participants were better able to use the full two seconds in the sequential task for own-race faces, but not for other-race faces. The purpose of Experiment 2 was to examine participants’ attentional allocation in complex scenes. Participants were shown scenes comprising people in real places, but the head stimuli used in Experiment 1 were superimposed onto the bodies in each scene. Using a Tobii eyetracker, participants’ looking time for both own- and other-race faces was evaluated to determine whether participants looked longer at own-race faces and whether individual differences in looking time correlated with individual differences in recognition accuracy. The results of this experiment demonstrated that although own-race faces were preferentially attended to in comparison to other-race faces, individual differences in looking time biases towards own-race faces did not correlate with individual differences in own-race recognition advantages. These results are also consistent with perceptual expertise, as it seems that the role of attentional biases towards own-race faces is independent of the cognitive processing that occurs for own-race faces. All together, these results have implications for face perception tasks that are performed in the lab, how accurate people may be when remembering faces in the real world, and the accuracy and patterns of errors in eyewitness testimony.

Psychocentricity and participant profiles: implications for lexical processing among multilinguals

Lexical processing among bilinguals is often affected by complex patterns of individual experience. In this paper we discuss the psychocentric perspective on language representation and processing, which highlights the centrality of individual experience in psycholinguistic experimentation. We discuss applications to the investigation of lexical processing among multilinguals and explore the advantages of using high-density experiments with multilinguals. High density experiments are designed to co-index measures of lexical perception and production, as well as participant profiles. We discuss the challenges associated with the characterization of participant profiles and present a new data visualization technique, that we term Facial Profiles. This technique is based on Chernoff faces developed over 40 years ago. The Facial Profile technique seeks to overcome some of the challenges associated with the use of Chernoff faces, while maintaining the core insight that recoding multivariate data as facial features can engage the human face recognition system and thus enhance our ability to detect and interpret patterns within multivariate datasets. We demonstrate that Facial Profiles can code participant characteristics in lexical processing studies by recoding variables such as reading ability, speaking ability, and listening ability into iconically-related relative sizes of eye, mouth, and ear, respectively. The balance of ability in bilinguals can be captured by creating composite facial profiles or Janus Facial Profiles. We demonstrate the use of Facial Profiles and Janus Facial Profiles in the characterization of participant effects in the study of lexical perception and production.