The Effect of Using Histogram Equalization and Discrete Cosine Transform on Facial Keypoint Detection

This study aims to figure out the effect of using Histogram Equalization and Discrete Cosine Transform (DCT) in detecting facial keypoints, which can be applied for 3D facial reconstruction in face recognition. Four combinations of methods comprising of Histogram Equalization, removing low-frequency coefficients using Discrete Cosine Transform (DCT) and using five feature detectors, namely: SURF, Minimum Eigenvalue, Harris-Stephens, FAST, and BRISK were used for test. Data that were used for test were obtained from Head Pose Image and ORL Databases. The result from the test were evaluated using F-score. The highest F-score for Head Pose Image Dataset is 0.140 and achieved through the combination of DCT & Histogram Equalization with feature detector SURF. The highest F-score for ORL Database is 0.33 and achieved through the combination of DCT & Histogram Equalization with feature detector BRISK

Face Image Quality Assessment: A Literature Survey

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions

A contribution for single and multiple faces recognition using feature-based approaches

Among biometric recognition systems, face biometrics plays an important role in research activities and security applications since face images can be acquired without any knowledge of individuals. Nowadays a huge amount of digital images and video sequences have been acquired mainly from uncontrolled conditions, frequently including noise, blur, occlusion and variation on scale and illumination. Because of these issues, face recognition (FR) is still an active research area and becomes a complex problem and a challenging task. In this context, the motivation comes from the fact that recognition of faces in digital images with complex background and databases of face images have become one of the successful applications of Computer Vision. Hence, the main goal of this work is to recognize one or more faces from still images with multiple faces and from a database of single faces obtained under different conditions. To work with multiple face images under varying conditions, a semi-supervised approach proposed based on the invariant and discriminative power of local features. The extraction of local features is done using Speeded-Up Robust Features (SURF). The search for regions from which optimal features can be extracted is fulfilled by an improved ABC algorithm. To fully exploit the proposed approach, an extensive experimental analysis was performed. Results show that this approach is robust and efficient for face recognition applications except for faces with non-uniform illumination. In the literature, a significant number of single FR researches are based on extraction of only one feature and machine learning approaches. Besides, existing feature extraction approaches broadly use either global or local features. To obtain relevant and complementary features from face images, a face recognition methodology should consider heterogeneous features and semi-global features. Therefore, a novel hierarchical semi-supervised FR approach is proposed based on extraction of global, semi-global and local features. Global and semi-global features are extracted using Color Angles (CA) and edge histogram descriptors (EHD) meanwhile only local features are extracted using SURF. An extensive experimental analysis using the three feature extraction methods was done first individually followed by a three-stage hierarchical scheme using the face images obtained under two different lighting conditions with facial expression and slight scale variation. Furthermore, the performance of the approach was also analyzed using global, semi-global and local features combinations for CA and EHD. The proposed approach achieves high recognition rates considering all image conditions tested in this work. In addition to this, the results emphasize the influence of local and semi-global features in the recognition performance. In both, single face and multiple faces approaches, the main achievement is the high performance obtained only from the discriminative capacity of extracted features without any training schemes.Entre os sistemas de reconhecimento biométrico, a biometria da face exerce um papel importante nas atividades de pesquisa e nas aplicações de segurança, pois a face pode ser obtida sem conhecimento prévio de um indivíduo. Atualmente, uma grande quantidade de imagens digitais e seqüências de vídeo têm sido adquiridas principalmente sob condições não-controladas, freqüentemente com ruído, borramento, oclusão e variação de escala e iluminação. Por esses problemas, o reconhecimento facial (RF) é ainda considerado como uma área de pesquisa ativa e uma tarefa desafiadora. A motivação vem do fato que o reconhecimento de faces nas imagens com fundo complexo e em base de imagens faciais tem sido uma aplicação de sucesso. Portanto, o principal foco deste trabalho é reconhecer uma ou mais faces em imagens estáticas contendo diversos indivíduos e um individuo (face) em uma base de imagens com faces únicas obtidas sob condições diferentes. Para trabalhar com faces múltiplas, uma abordagem semi-supervisionada foi proposta baseada em características locais invariantes e discriminativas. A extração de características (EC) locais é feita utilizando-se do algoritmo Speeded-Up Robust Features (SURF). A busca por regiões nas quais as características ótimas podem ser extraídas é atendida através do algoritmo ABC. Os resultados obtidos mostram que esta abordagem é robusta e eficiente para aplicações de RF exceto para faces com iluminação não-uniforme. Muitos trabalhos de RF são baseados somente na extração de uma característica e nas abordagens de aprendizagem de máquina. Além disso, as abordagens existentes de EC usam características globais e/ou locais. Para obter características relevantes e complementares, a metodologia de RF deve considerar também as características de diferentes tipos e semi-globais. Portanto, a abordagem hierárquica de RF é proposta baseada na EC como globais, semi-globais e locais. As globais e semi-globais são extraídas utilizando-se de Color Angles (CA) e Edge Histogram Descriptors (EHD) enquanto somente características locais são extraídas utilizando-se do SURF. Uma ampla análise experimental foi feita utilizando os três métodos individualmente, seguido por um esquema hierárquico de três - estágios usando imagens faciais obtidas sob duas condições diferentes de iluminação com expressão facial e uma variação de escala leve. Além disso, para CA e EHD, o desempenho da abordagem foi também analisado combinando-se características globais, semi-globais e locais. A abordagem proposta alcança uma taxa de reconhecimento alta com as imagens de todas as condições testadas neste trabalho. Os resultados enfatizam a influência das características locais e semi-globais no desempenho do reconhecimento. Em ambas as abordagens, tanto nas faces únicas quanto nas faces múltiplas, a conquista principal é o alto desempenho obtido somente com a capacidade discriminativa de características sem nenhum esquema de treinamento

Evaluation methodologies for security testing biometric systems beyond technological evaluation

The main objective of this PhD Thesis is the specification of formal evaluation methodologies for testing the security level achieved by biometric systems when these are working under specific contour conditions. This analysis is conducted through the calculation of the basic technical biometric system performance and its possible variations. To that end, the next two relevant contributions have been developed. The first contribution is the definition of two independent biometric performance evaluation methodologies for analysing and quantifying the influence of environmental conditions and human factors respectively. From the very beginning it has been claimed and demonstrated that these two contour conditions are the most significant parameters that may affect negatively the biometric performance. Nevertheless, in spite of ISO/IEC 19795 standard [ISO'06b], which addresses biometric performance testing and reporting, being published in 2006, no evaluation methodology for assessing such adverse effects has been implemented yet. Therefore, this dissertation proposes both methodologies which have been defined in accordance to the following requirements: - should be general and modality independent for covering the analysis of all kind of biometric systems; - should conform to the principles and requirements already defined in ISO/IEC 19795 multipart standard; and - should provide requirements and procedures to accurately define the evaluation conditions to be tested, conduct reproducible test methods and obtain objective and intercomparable results. The second relevant contribution is the development of detailed guidelines for addressing how to conduct biometric performance evaluations in compliance with Common Criteria [CC]. Common Criteria is currently the only international recognised evaluation framework with which developers have to analyse and demonstrate the level of security achieved by their products. However, the applicability of this methodology to biometrics needs the specification of supplementary guidelines. As a consequence, this dissertation proposes such guidelines which have been specified according to the following requirements: - should be independent of any biometric modality; - should be based on previous works published in this topic BTSE [BTSE'01], BEM [BEM'02] and the ISO/IEC 19792 international standard which addresses security evaluation of biometric system; - should conform to the last version of both Common Criteria and the ISO/IEC 19795 multipart standards; and - should cover those kinds of biometric performance evaluations that can be repeatable, i.e. technology and scenario evaluations as well as the Common Criteria evaluation activities involved in the execution of such test procedures. As for the evaluation of the security of biometric systems there is the need of determine their performance, and as such performance also depends on contour conditions, both evaluation methodologies (i.e. environmental and human factors) and Common Criteria guidelines, are merged in order to provide improved evaluation methodology for the security of biometric systems. ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El objetivo principal de esta Tesis Doctoral es la especificación de metodologías de evaluación formales para analizar el nivel de seguridad alcanzado por los sistemas biométricos cuando estos se encuentran trabajando bajo condiciones de contorno específicas. Este análisis se realiza a través del cálculo del rendimiento técnico básico del sistema biométrico y sus posibles variaciones. A tal efecto, se han elaborado las siguientes contribuciones. En primer lugar, se han especificado dos metodologías de evaluación de rendimiento biométrico de manera independiente para analizar y cuantificar la influencia de las condiciones ambientales y los factores humanos, respectivamente. Desde los primeros estudios sobre rendimiento biométrico, se ha afirmado y demostrado que éstos son los parámetros más significativos que pueden afectar negativamente al rendimiento biométrico. No obstante, a pesar de que la norma ISO/IEC 19795 que regula la evaluación y documentación del rendimiento de los sistemas biométricos fue publicada en 2006, ninguna metodología que evalúe dichos efectos adversos ha sido implementada hasta el momento. Por lo tanto la presente Tesis Doctoral propone ambas metodologías, las cuáles han sido definidas conforme a las siguientes condiciones: - son de carácter general e independientes de cualquier modalidad biométrica para cubrir el análisis de todo tipo de sistemas biométricos, - cumplen con los principios y requisitos previamente definidos en la norma internacional ISO/IEC 19795 [ISO'06b], y - proporcionan requisitos y procedimientos detallados para: definir las condiciones de los ensayos, efectuar métodos de ensayo reproducibles y obtener resultados objetivos e intercomparables. En segundo lugar, se han desarrollado directrices específicas que abordan la forma de realizar evaluaciones de rendimiento biométrico conforme a "Common Criteria for IT security evaluation" (conocido habitualmente como "Common Criteria" [CC]). Common Criteria es actualmente el único marco de evaluación internacionalmente reconocido del que disponen los desarrolladores de sistemas biométricos para analizar y demostrar el nivel de seguridad que alcanzan sus productos. Sin embargo, la aplicación de esta metodología a la tecnología biométrica requiere la especificación de pautas complementarias. Por consiguiente, esta Tesis Doctoral propone tales pautas o directrices, las cuáles se han especificado de acuerdo con los siguientes requisitos: - son independientes de cualquier modalidad biométrica, - se basan en los trabajos previos que ya han sido publicados en esta área tales como BTSE [BTSE'01], BEM [BEM'02] y el estándar internacional ISO/IEC 19792 [ISO'09a] que regula la evaluación de seguridad de los sistemas biométricos, - son conformes a las últimas versiones tanto de Common Criteria como de la norma internacional ISO/IEC 19795, y - cubren tanto el tipo de evaluaciones de rendimiento biométrico que pueden ser repetibles, es decir las evaluaciones tecnológicas y de escenario, como las actividades de evaluación establecidas por la norma Common Criteria que conllevan la realización de dichos procedimientos de test. Debido a que es necesario determinar el rendimiento de los sistemas biométricos para evaluar su seguridad, y ya que dicho rendimiento depende de distintas condiciones de contorno, las dos metodologías de evaluación previamente definidas (condiciones ambientales y factores humanos) se han unido con las directrices de Common Criteria, para así conseguir una mejora sustancial en la metodología de evaluación de la seguridad de los sistemas biométricos