Asymmetrical Half-join Method on Dual Vision Face Recognition

Abstract

This research proposes a model of face recognition using the method of joining two face images from left and right lens from a stereo vision camera namely half-join method. Half-join method is used in face image normalization processing. The proposed half-join method is a face images joining model, which is called asymmetrical half-join. In asymmetrical half-join method, a RoI (region of interest) of face image from left and right lenses are provided based on axis center of each eye in eye detection. The cropping of face image from asymmetrical half-join model has different width depends on eyes coordinate location. The proposed system shows that the asymmetrical half-join method can produce a better of face recognition rate. The experimental results show that the asymmetrical half-join method has a better recognition rate and computation time than single vision method and symmetrical half-join method.

Human face detection and recognition using contour generation and matching algorithm

“…CBIR performs image search by image feature itself. Based on real time trial results, the accuracy value obtained is 61.64% [9]. I Nyoman Gede Arya Astawa & et al, 2018, in this experiment, intensity normality method in one of the color space channel and tested the faces using Android based have been developed.…”

Face recognition using fractional coefficients and discrete cosine transform tool

“…The basic encoding operation of the DCT is as follows: The lower right DCT coefficients represent higher frequencies, and are often sufficiently small to be discarded with little visible distortion. The DCT coefficients are calculated using (2), and the matrix representing DCT coefficients can be divided into three matrix namely low, middle and high frequencies.…”

“…In the last decades, face and facial expressions recognition has received crucial attention from the worldwide research community, and it is a popular scope in data and object recognition and computer application according to its enormous and motivated range of security and criminal applications such as forensic face recognition [1][2][3], biometric authentication [4,5], video surveillance [6], information security [7] and edge detection [8]. Face recognition system is mostly used to perform verification of human identity, this way is based on feature extraction and dimensionality reduction, and a number of facial recognition systems have been produced with distinct measure of success.…”

Aplicación web para el análisis de emociones y atención de estudiantes

“…Para la selección de las metodologías que permiten el reconocimiento de rostros, se estudiaron los métodos de visión artificial propuestas en [19]- [25] analizando sus pros y sus contras. Se concluyó que las metodologías presentadas en [23], [24] se adaptan mejor al problema que se quiere resolver y presentan, además, un bajo costo computacional. Consecuentemente, se implementaron los siguientes modelos: Modelo 1: este modelo se basa en el algoritmo Haar-cascade, propuesto por [25] y que fue diseñado para reconocer objetos en una imagen.…”