Browsing by Author "Saxena, Amit"

Now showing 1 - 2 of 2

A robust real-time facial alignment system with facial landmarks detection and rectification for multimedia applications
(1970-01-01)
Chou, Kuang Pen
;
Prasad, Mukesh
;
Yang, Jie
;
Su, Sheng-Yao
;
Tao, Xian
Face detection often plays the first step in various visual applications. Large variants of facial deformations due to head movements and facial expression make it difficult to identify appropriate face region. In this paper, a robust real-time face alignment system, including facial landmarks detection and face rectification, is proposed. A facial landmarks detection model based on regression tree is utilized in the proposed system. In face rectification framework, 2-D geometrical analysis based on pitch, yaw and roll movements is designed to solve the misalignment problem in face detection. The experiments on the two datasets verify the performance significantly improved by the proposed method in the facial recognition task and outperform than those obtained by other alignment methods. Furthermore, the proposed method can achieve robust recognition results even if the amount of training images is not large.
An Improved Polynomial Neural Network Classifier Using Real-Coded Genetic Algorithm
(2015-11-01)
Lin, Chin-Teng
;
Prasad, Mukesh
;
Saxena, Amit
;
資訊工程學系
;
電機工程學系
In this paper, a novel approach is proposed to improve the classification performance of a polynomial neural network (PNN). In this approach, the partial descriptions (PDs) are generated at the first layer based on all possible combinations of two features of the training input patterns of a dataset. The set of PDs from the first layer, the set of all input features, and a bias constitute the chromosome of the real-coded genetic algorithm (RCGA). A system of equations is solved to determine the values of the real coefficients of each chromosome of the RCGA for the training dataset with the mean classification accuracy (CA) as the fitness value of each chromosome. To adjust these values for unknown testing patterns, the RCGA is iterated in the usual manner using simple selection, crossover, mutation, and elitist selection. The method is tested extensively with the University of California, Irvine benchmark datasets by utilizing tenfold cross validation of each dataset, and the performance is compared with various well-known state-of-the-art techniques. The results obtained from the proposed method in terms of CA are superior and outperform other known methods on various datasets.