Jesus Olivares-Mercado

A sub-block-based eigenphases algorithm with optimum sub-block size

Several algorithms have been proposed for constrained face recognition applications. Among them the eigenphases algorithm and some variations of it using sub-block processing, appears to be desirable alternatives because they achieves high face recognition rate, under controlled conditions. However, their performance degrades when the face images under analysis present variations in the illumination conditions as well as partial occlusions. To overcome these problems, this paper derives the optimal sub-block size that allows improving the performance of previously proposed eigenphases algorithms. Theoretical and computer evaluation results show that, using the optimal block size, the identification performance of the eigenphases algorithm significantly improves, in comparison with the conventional one, when the face image presents different illumination conditions and partial occlusions respectively. The optimal sub-block size also allows achieving a very low false acceptance and false rejection rates, simultaneously, when performing identity verification tasks, which is not possible to obtain using the conventional approach; as well as to improve the performance of other sub-block-based eigenphases methods when rank tests are performed.

Improving the eigenphase method for face recognition

This paper proposes an improvement to the Eigenphases method, in which the image is normalized to reduce the illumination and facial expression effects and the Principal Components Analysis (PCA) is used for feature extraction, while the Gaussian Mixture Model (GMM)is used to improve the performance of classification stage. An important advantage of GMM is that this system is trained without supervisor and constructs an independent model for each user. The proposed method is evaluated using the ”AR Face Database”, which includes the face images of 120 subjects (65 males and 55 females). Evaluation results show that the proposed method provides better performance than the original eigenphases method.

Feature extraction and face verification using Gabor and Gaussian mixture models

This paper proposes a faces verification in which the feature extraction is carried out using the discrete Gabor function (DGF), while the Gaussian Mixture Model (GMM) is used in the face verification stage. Evaluation results using standard data bases with different parameters, such as the number of mixtures and the number of face used for training show that proposed system provides better results that other proposed systems with a correct verification rate larger than 95%. Although, as happens in must face recognition systems, the verification rate decreases when the target faces present some rotation degrees.

Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis

This paper proposes a view-invariant gait recognition framework that employs a unique view invariant model that profits from the dimensionality reduction provided by Direct Linear Discriminant Analysis (DLDA). The framework, which employs gait energy images (GEIs), creates a single joint model that accurately classifies GEIs captured at different angles. Moreover, the proposed framework also helps to reduce the under-sampling problem (USP) that usually appears when the number of training samples is much smaller than the dimension of the feature space. Evaluation experiments compare the proposed framework’s computational complexity and recognition accuracy against those of other view-invariant methods. Results show improvements in both computational complexity and recognition accuracy.

GMM vs SVM for Face Recognition and Face Verification

The security is a theme of active research in which the identification and verification identity of persons is one of the most fundamental aspects nowadays. Face recognition is emerging as one of the most suitable solutions to the demands of recognition of people. Face verification is a task of active research with many applications from the 80’s. It is perhaps the biometric method easier to understand and non-invasive system because for us the face is the most direct way to identify people and because the data acquisition method consist basically on to take a picture. Doing this recognition method be very popular among most of the biometric systems users. Several face recognition algorithms have been proposed, which achieve recognition rates higher than 90% under desirable’s condition (Chellapa et al., 2010; Hazem & Mastorakis, 2009; Jain et al., 2004; Zhao et al., 2003). The recognition is a very complex task for the human brain without a concrete explanation. We can recognize thousands of faces learned throughout our lives and identify familiar faces at first sight even after several years of separation. For this reason, the Face Recognition is an active field of research which has different applications. There are several reasons for the recent increased interest in face recognition, including rising public concern for security, the need for identity verification in the digital world and the need for face analysis and modeling techniques in multimedia data management and computer entertainment. Recent advances in automated face analysis, pattern recognition, and machine learning have made it possible to develop automatic face recognition systems to address these applications (Duda et al., 2001). This chapter presents a performance evaluation of two widely used classifiers such as Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) for classification task in a face recognition system, but before beginning to explain about the classification stage it is necessary to explain with detail the different stages that make up a face recognition system in general, to understand the background before using the classifier, because the stages that precede it are very important for the proper operation of any type of classifier.

Detección de Movimiento de Vehículos en Secuencias de Video Basados en la Diferencia Absoluta entre Fotogramas y la Combinación de Bordes

Resumen Se presenta un metodo para la deteccion de movimiento en secuencias de video, mediante la combinacion de la Diferenciacion Absoluta entre dos Fotogramas (DAF) y el analisis de bordes en la region considerada movimiento en el tiempo t. Esto permite resolver el fenomeno de apertura del primer plano al unir las regiones que pertenecen a un objeto en movimiento, una de las principales desventajas de la DAF. Las imagenes que son consideradas objetos de interes se someten a operaciones morfologicas para eliminar objetos con componentes conectados menores a un umbral dado. Se realiza una segunda diferenciacion absoluta de fotogramas considerando solo las regiones determinadas con movimiento en la primera diferenciacion, de manera que al combinar los bordes con la deteccion de movimiento de la segunda diferencia absoluta, se obtiene la imagen del primer plano. El algoritmo desarrollado tiene la ventaja de presentar la forma y principales parametros del objeto en movimiento.

Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

In recent years, online social media information has been the subject of study in several data science fields due to its impact on users as a communication and expression channel. Data gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users’ opinions and make predictions about real events. Cyber-attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber-attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

A cheating-prevention mechanism for hierarchical secret-image-sharing using robust watermarking

Secret image sharing (SIS) techniques allow visual secrets to be shared between multiple people. These techniques require a predetermined access structure to be satisfied to reveal the secret. SIS schemes have the potential to increase security in several applications including telemedicine and image transfer in the cloud computing environment, providing controlled access to confidential images. To date, a significant number of SIS schemes with various properties and access structures have been proposed. Among them, hierarchical SIS (HSIS) is considered foremost since almost all organizations and associations manage their secret information in a hierarchical manner. However, the HSIS scheme tends to suffer from security flaws as the secret image can often be visually leaked, even when the access structure is not properly satisfied. To avoid this security flaw, we propose a cheating prevention mechanism by introducing a robust watermarking technique based on the Quantization Index Modulation-Dither Modulation (QIM-DM) algorithm in the discrete cosine transform (DCT) domain. Experimental results demonstrate the superior effectiveness of our proposed cheating prevention mechanism. Furthermore, our approach retains all of the desirable properties of the HSIS scheme.

Face recognition system based on MOTIF features

ABSTRACT This work addresses the use of the MOTIF algorithm for face feature extraction. The MOTIF algorithm is commonly used to characterize texture and shows good performance in this task; a MOTIF algorithm without the Co-occurrence Matrix is proposed to obtain face features, and the approach proves to be effective. System testing was based on a standard database (the AR Face database) that includes 120 people, 70 images with face expressions and 30 with sunglasses; 1 to 9 images were used to make the template for each person. After using Euclidean distance, Cosine distance and support vector machine as classifiers, correct classification was achieved with 98% accuracy. Further tests were performed with all databases and compared with Local Binary Pattern, DI-WBP and other commonly used schemes, demonstrating effective face recognition by the MOTIF algorithm without the co-occurrence matrix in addition to its fast performance due to the low computational cost.

A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis

This paper proposes a view-invariant gait recognition algorithm, which builds a unique view invariant model taking advantage of the dimensionality reduction provided by the Direct Linear Discriminant Analysis (DLDA). Proposed scheme is able to reduce the under-sampling problem (USP) that appears usually when the number of training samples is much smaller than the dimension of the feature space. Proposed approach uses the Gait Energy Images (GEIs) and DLDA to create a view invariant model that is able to determine with high accuracy the identity of the person under analysis independently of incoming angles. Evaluation results show that the proposed scheme provides a recognition performance quite independent of the view angles and higher accuracy compared with other previously proposed gait recognition methods, in terms of computational complexity and recognition accuracy.