Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accurac...

In this paper we describe experiments using eigenfaces for recognition and interactive search in the FERET face database. A recognition accuracy of 99.35% is obtained using frontal views of 155 individuals. This gure is consistent with the 95% recognition rate obtained previously on a much larger database of 7,562 \mugshots" of approximately 3,000 individuals, consisting of a mix of all age and...

A novel approach for content-based image retrieval and its specialization to face recognition are described. While most face recognition techniques aim at modeling faces, our goal is to model the transformation between face images of the same person. As a global face transformation may be too complex to be modeled directly, it is approximated by a collection of local transformations with a cons...

Face hallucination refers to inferring an High-Resolution (HR) face image from the input Low-Resolution (LR) one. It plays a vital role in LR face recognition by both manual and computer. The eigentransformation method based on Principal Component Analysis (PCA), which represents face image as a linear combination of the eigenfaces, has attracted considerable interests because of its simplicity...

Face recognition is one of the most relevant applications of image analysis. It’s an efficient task (true challenge) to build an automated system with equal human ability to face recognised. Face is a complex 3D visual model and developing a computational model for face recognition is a difficult task. The paper presents a methodology for face recognition based on information theory approach of...

A hierarchical representation consisting of two levels linear combinations (LC) is proposed for face recognition. At the first level, a face image is represented as a linear combination (LC) of a set of basis vectors, i.e. eigenfaces. Thereby a face image corresponds to a feature vector (prototype) in the eigenface space. Normally several such prototypes are available for a face class, each rep...

A system that uses an underlying genetic algorithm to evolve faces in response to user selection is described. The descriptions of faces used by the system are derived from a statistical analysis of a set of faces. The faces used for generation are transformed to an average shape by defining locations around each face and morphing. The shape-free images and shape vectors are then separately sub...

In this paper we propose the discrete cosine transform (DCT) mod 2 feature set, which utilizes polynomial coe2cients derived from 2D DCT coe2cients obtained from spatially neighboring blocks. Face veri%cation results on the multi-session VidTIMIT database suggest that the DCT-mod 2 feature set is superior (in terms of robustness to illumination direction changes and discrimination ability) to f...

This paper proposes a Face Recognition Algorithm in which the Discrete Gabor transform is used to extract the image face features vector that is then feed into a multilayer perceptron to carried out the recognition task. The features vector, estimated using the Gabor Transform, presents a small intra-person variation while the inter-persons variation is considerably large. This fact provides ro...

Introduction: This article is the result of research entitled "Development a prototype to optimize access conditions SENA-Pescadero using artificial intelligence and open-source tools", developed at Servicio Nacional de Aprendizaje in 2020. Problem: How identify Machine Learning Techniques applied computer vision processes through literature review? Objective: Determine application, as well adv...