Keshav Seshadri

Learn More
In this paper we present an improved method for locating facial landmarks in images containing frontal faces using a modified Active Shape Model. Our main contributions include the use of an optimal number of facial landmark points, better profiling methods during the fitting stage and the development of a more suitable optimization metric to determine the(More)
In this paper we propose a novel Contourlet Appearance Model (CAM) that is more accurate and faster at localizing facial landmarks than Active Appearance Models (AAMs). Our CAM also has the ability to not only extract holis­ tic texture information, as AAMs do, but can also extract local texture information using the Nonsubsampled Con­ tourlet Transform(More)
In this paper we address the problem of automatically locating the facial landmarks of a single person across frames of a video sequence. We propose two methods that utilize Kalman filter based approaches to assist an Active Shape Model (ASM) in achieving this goal. The use of Kalman filtering not only aids in better initialization of the ASM by predicting(More)
In this paper, we propose a novel system for beard and mustache detection and segmentation in challenging facial images. Our system first eliminates illumination artifacts using the self-quotient algorithm. A sparse classifier is then used on these self-quotient images to classify a region as either containing skin or facial hair. We conduct experiments on(More)
The harmful effects of cell phone usage on driver behavior have been well investigated and the growing problem has motivated several several research efforts aimed at developing automated cell phone usage detection systems. Computer vision based approaches for dealing with this problem have only emerged in recent years. In this paper, we present a vision(More)
Active Shape Models (ASMs) have recently gained popularity for performing automatic facial landmark fitting. Their demonstrated ability to generalize and fit unseen faces make them ideal candidates for this task unlike the traditional Active Appearance Model (AAM)-based approaches, which have difficulty in accurately landmarking unseen images. Given a test(More)
Biometric recognition based on the characteristics of human faces has attracted a great deal of attention over the past few years. However, the similarity in the facial appearance of identical twins has made the task difficult and has even compromised commercial face recognition systems. In this paper, we shed new light on the study of facial recognition of(More)
Traditional approaches to face recognition have utilized aligned facial images containing both shape and texture information. This paper analyzes the contributions of the individual facial shape and texture components to face recognition. These two components are evaluated independently and we investigate methods to combine the information gained from each(More)
A reliable and accurate biometric identification system must be able to distinguish individuals even in situations where their biometric signatures are very similar. However, the strong similarity in the facial appearance of twins has complicated facial feature based recognition and has even compromised commercial face recognition systems. This paper(More)
This paper presents Adaptive Population Sizing Genetic Algorithm (AGA) assisted Maximum Likelihood (ML) estimation of Orthogonal Frequency Division Multiplexing (OFDM) symbols in the presence of Nonlinear Distortions. The proposed algorithm is simulated in MATLAB and compared with existing estimation algorithms such as iterative DAR, decision feedback(More)