Face Detection using SVM Trained in Eigenfaces Space

The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling

Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time

Face Detection Using an SVM Trained in Eigenfaces Space

Abstract. 1 The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling.

Verificación de vehículos mediante técnicas de visión artificial

En este trabajo, se proponen sistemas de verificación de vehículos mediante métodos basados en aprendizaje. En primer lugar se realiza un estudio del estado del arte para conocer los problemas actuales en la materia. Después, se muestra la arquitectura de los sistemas que se divide en dos etapas: extracción de características y clasificación. En la primera etapa se realiza una breve exposición de los tipos de características que se van a implementar (simetría, bordes, análisis de componentes principales (PCA) e histogramas de gradientes orientados (HOG)). La etapa de clasificación consiste en una explicación teórica de los clasificadores utilizados en nuestro sistema. Posteriormente, se realiza el desarrollo de estos sistemas, efectuando mejoras para cada uno de ellos. Para el sistema basado en simetría se plantean dos métodos diferentes, introduciéndose una mejora en el segundo método, que consiste en una diferenciación entre ejes compuestos por uno y dos píxeles, junto con una penalización en los valores de simetría para conseguir una mayor diferenciación entre las clases. Respecto al sistema basado en bordes, se utilizan únicamente bordes verticales, donde se analiza el uso de vectores reducidos. Por otra parte, se presenta el uso de la matriz de correlaciones para desarrollar el sistema basado en PCA. En el sistema basado en HOG se estudia qué parámetros son los adecuados para el descriptor en el caso particular de vehículos, proponiéndose descriptores eficientes basados en esta configuración, que pueden ser implementados en sistemas en tiempo real. Finalmente, con los resultados obtenidos en el paso previo se procede a un análisis para los distintos métodos presentando sus principales características y limitaciones.In this work, a vehicle verification systems using learning methods are proposed. First, a study of related work has been done. Afterwards, the arquitecture of these systems is explained. The arquitecure is divided in two stages: feature extraction and clasification. In the first stage, a brief summary of the different features that will be implemented (simmetry, edges, principal components analysis (PCA) and histograms of oriented gradients (HOG)) is given. The second stage is a theoretical explanation of the classifiers used in this system. Subsequently, the systems are developed with new improvements. Two different methods are proposed for the system based on symmetry. An improvement is introduced for the second method that is a differentiation between compounds axes by one and two pixels, also a penalty is introduced into the values of symmetry for greater differentiation between classes. Regarding the system based on edges, vertical edges are used, where the performance reducing the size of the vectors is analyzed. Moreover, the correlation matrix is used to develop the system based on PCA. In the system based on HOG, in the particular case of vehicles, appropiate parameters for the descriptor are studied, proposing efficient descriptors based on this configuration that can be implemented in real-time systems. Finally, the results obtained in the previous step are analyzed for each of the methods, and their main characteristics and limitations are described