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Mobile device features such as camera and other sensors are evolving rapidly nowadays. Supported by a reliable communications network, it raises new methods in information retrieval. Mobile devices can capture an image with its camera and pass it to the retrieval systems to get the information needed. This system, called Mobile Content-Based Image Retrieval (MCBIR), generally consists of two parts:...
B-cell conformational epitope identification is the crucial issue in vaccinology. Limitation on experimental methods in the biological side, dataset and availability of computational resources opens a chance on developing prediction method which can accelerate epitope identification. A number of methods have been developed but their performance is still medium. Epitope prediction is a knowledge-based...
Genetic algorithm is a population-based evolutionary optimization approach, and this has been used to train artificial neural networks successfully. When a large number of individuals make up the population, however, the running time of the algorithm often becomes very long. Parallel computation is a technique that can potentially be used to address this issue. This research investigates the adoption...
Facial landmarks play an important role in face recognition. They serve different steps of the recognition such as pose estimation, face alignment, and local feature extraction. Recently, cascaded shape regression has been proposed to accurately locate facial landmarks. A large number of weak regressors are cascaded in a sequence to fit face shapes to the correct landmark locations. In this paper,...
To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this...
We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities...
Facial expression is one of the main issues of face recognition in uncontrolled environments. In this paper, we apply the probabilistic linear discriminant analysis (PLDA) method to recognize faces across expressions. Several PLDA approaches are tested and cross-evaluated on the Cohn-Kanade and JAFFE databases. With less samples per gallery subject, high recognition rates comparable to previous works...
We propose an approach to employ eigen light-fields for face recognition across pose on video. Faces of a subject are collected from video frames and combined based on the pose to obtain a set of probe light-fields. These probe data are then projected to the principal subspace of the eigen light-fields within which the classification takes place. We modify the original light-field projection and found...
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