Robust Pose Invariant Shape and Texture based Hand Recognition

TL;DR

This paper introduces a robust, pose-invariant hand recognition system utilizing shape and texture features from segmented palm and finger images, achieving over 98% accuracy in identification and verification.

Contribution

The paper presents a fully automatic hand segmentation and pose normalization method combined with a novel fusion of shape and texture features for improved hand recognition accuracy.

Findings

Achieved over 98% accuracy in hand identification and verification.

Developed automatic segmentation and pose normalization algorithms.

Demonstrated superior performance over existing systems.

Abstract

This paper presents a novel personal identification and verification system using information extracted from the hand shape and texture. The system has two major constituent modules: a fully automatic and robust peg free segmentation and pose normalisation module, and a recognition module. In the first module, the hand is segmented from its background using a thresholding technique based on Otsu`s method combined with a skin colour detector. A set of fully automatic algorithms are then proposed to segment the palm and fingers. In these algorithms, the skeleton and the contour of the hand and fingers are estimated and used to determine the global pose of the hand and the pose of each individual finger. Finally the palm and fingers are cropped, pose corrected and normalised. In the recognition module, various shape and texture based features are extracted and used for matching purposes. The modified Hausdorff distance, the Iterative Closest Point (ICP) and Independent Component Analysis (ICA) algorithms are used for shape and texture features of the fingers. For the palmprints, we use the Discrete Cosine Transform (DCT), directional line features and ICA. Recognition (identification and verification) tests were performed using fusion strategies based on the similarity scores of the fingers and the palm. Experimental results show that the proposed system exhibits a superior performance over existing systems with an accuracy of over 98\% for hand identification and verification (at equal error rate) in a database of 560 different subjects.