We review several model-based and image-based methods that we have developed for analyzing, synthesizing, and recognizing facial images. Our model-based methods include a sophisticated, functional model of the human face/head, which incorporates a biomechanical tissue model with embedded muscle actuators, and techniques for applying it to computer animation and expression estimation in video. O...

Facial reconstruction using 3-D computer graphics is being used in our institute as a routine procedure in forensic cases as well as for skulls of historical and archaeological interest. Skull and facial data from living subjects is acquired using an optical laser scanning system. For the production of the reconstructed image, we employ facial reconstruction software which is constructed using ...

In this paper, we investigate the effectiveness of the Extreme Learning Machine (ELM) network in facial image classification. In order to enhance performance, we exploit knowledge related to the human face structure. We train a multi-view ELM network by employing automatically created facial regions of interest to this end. By jointly learning the network parameters and optimized network output...

A system for detecting and analyzing behavior of a sports person from their facial expression extracted from a sports video from the basis of this project. Shot Segmentation, Object Frame Selection, Image Segmentation, Facial Feature Extraction and Facial Expression Recognition (FER) are the major steps included in developing Cognitive Analysis of Facial Expression Recognition system from Sport...

Facial expressions play a major role in Face Recognition Systems and image processing techniques of Human Machine Interface. There are several techniques for facial features selection like Principal Component Analysis, Distance calculation among face components, Template Matching. This algorithm describes a simple template matching based facial feature selection technique and detects facial exp...

Facial expression has been central to the study of emotion < cross-ref to emotion > for over a hundred years (Darwin, 1872/1998). Much of what we have learned was made possible by technological breakthroughs: photography in the nineteenth century and film and later video in the twentieth. Today, two new technologies are just beginning to make their p...

An approach to the analysis and representation of facial dynamics for recognition of facial expressions from image sequences is proposed. The algorithms we develop utilize optical ow computation to identify the directions of rigid and non-rigid motions that are caused by human facial expressions. A mid-level symbolic representation motivated by linguistic and psychological considerations is dev...

An approach for analysis and representation of facial dynamics for recognition of facial expressions from image sequences is proposed. The algorithms we develop utilize optical ow computation to identify the direction of rigid and non-rigid motions that are caused by human facial expressions. A mid-level symbolic representation that is motivated by linguistic and psychological considerations is...

While features used for texture analysis have been successfully used in some biometric applications, only quite few works have considered them in facial image analysis. Texture-based region descriptors can be very useful in recognizing faces and facial expressions, detecting faces and different facial components, and in other face related tasks. This paper demonstrates this issue by considering...

Capturing and analyzing the correlations among facial parts are important for interpreting facial behaviors precisely. In this paper, we exploit Canonical Correlation Analysis (CCA) to model the correlations of facial parts for facial expression analysis. We propose a Matrix-based Canonical Correlation Analysis (MCCA) for better correlation analysis on 2D image or matrix data in general. Extens...