This paper presents advances on the Human ID Gait Challenge. Our method is based on combining an improved gait recognition method with an adapted low resolution face recognition method. For this, we experiment with a new automated segmentation technique based on alpha-matting. This allows better construction of feature images used for gait recognition. The same segmentation is also used as a basis for finding and recognizing low-resolution facial profile images in the same database. Both, gait and face recognition methods show results comparable to the state of the art. Next, the two approaches are fused (which to our knowledge, has not yet been done for the Human ID Gait Challenge). With this fusion gain, we show significant performance improvement. Moreover, we reach the highest recognition rates and the largest absolute number of correct detections to date. © 2012 IEEE.

A. N. Rajagopalan

Department of Electrical Engineering

ABSOLUTE NUMBER

Automated segmentation

FACE RECOGNITION METHODS

FACIAL PROFILES

FEATURE IMAGES

GAIT RECOGNITION

LOW RESOLUTION

Performance improvements

Recognition rates

State of the art

Biometrics

Gait analysis

image segmentation

Face recognition

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Combined face and gait recognition using alpha matte preprocessing

Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012

The focus of this paper is on the problem of recognizing faces across space-varying motion blur, changes in pose, illumination, and expression, as well as partial occlusion, when only a single image per subject is available in the gallery. We show how the blur, incurred due to relative motion between the camera and the subject during exposure, can be estimated from the alpha matte of pixels that straddle the boundary between the face and the background. We also devise a strategy to automatically generate the trimap required for matte estimation. Having computed the motion via the matte of the probe, we account for pose variations by synthesizing from the intensity image of the frontal gallery a face image that matches the pose of the probe. To handle illumination, expression variations, and partial occlusion, we model the probe as a linear combination of nine blurred illumination basis images in the synthesized nonfrontal pose, plus a sparse occlusion. We also advocate a recognition metric that capitalizes on the sparsity of the occluded pixels. The performance of our method is extensively validated on synthetic as well as real face data. © 2016 Optical Society of America.

Journal of the Optical Society of America A: Optics and Image Science, and Vision

Recognizing blurred, nonfrontal, illumination, and expression variant partially occluded faces

We propose a view-based approach to recognize humans from their gait. Two different image features have been considered: The width of the outer contour of the binarized silhouette of the walking person and the entire binary silhouette itself. To obtain the observation vector from the image features, we employ two different methods. In the first method, referred to as the indirect approach, the high-dimensional image feature is transformed to a lower dimensional space by generating what we call the frame to exemplar (FED) distance. The FED vector captures both structural and dynamic traits of each individual. For compact and effective gait representation and recognition, the gait information in the FED vector sequences is captured in a hidden Markov model (HMM). In the second method, referred to as the direct approach, we work with the feature vector directly (as opposed to computing the FED) and train an HMM. We estimate the HMM parameters (specifically the observation probability B) based on the distance between the exemplars and the image features. In this way, we avoid learning high-dimensional probability density functions. The statistical nature of the HMM lends overall robustness to representation and recognition. The performance of the methods is illustrated using several databases. © 2004 IEEE.

IEEE Transactions on Image Processing

Identification of humans using gait

IEEE Transactions on Circuits and Systems for Video Technology

Face and Human Gait Recognition Using Image-to-Class Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence

A Closed-Form Solution to Natural Image Matting

Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content- Based Image Retrieval

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

Journal	Proceedings - 2012 5th IAPR International Conference on Biometrics, ICB 2012
Open Access	Yes