Multi-modal ear and face modeling and recognition (original) (raw)

Score-Level Fusion of 3D Face and 3D Ear for Multimodal Biometric Human Recognition

Computational Intelligence and Neuroscience

A novel multimodal biometric system is proposed using three-dimensional (3D) face and ear for human recognition. The proposed model overcomes the drawbacks of unimodal biometric systems and solves the 2D biometric problems such as occlusion and illumination. In the proposed model, initially, the principal component analysis (PCA) is utilized for 3D face recognition. Thereafter, the iterative closest point (ICP) is utilized for 3D ear recognition. Finally, the 3D face is fused with a 3D ear using score-level fusion. The simulations are performed on the Face Recognition Grand Challenge database and the University of Notre Dame Collection F database for 3D face and 3D ear datasets, respectively. Experimental results reveal that the proposed model achieves an accuracy of 99.25% using the proposed score-level fusion. Comparative analyses show that the proposed method performs better than other state-of-the-art biometric algorithms in terms of accuracy.

Human Identification based on 3D Ear Models

2007

Two 3D ear recognition systems using structure from motion (SFM) and shape from shading (SFS) techniques, respectively, are explored. Segmentation of the ear region is performed using interpolation of ridges and ravines identified in each frame in a video sequence. For the SFM system, salient features are tracked across the video sequence and are reconstructed in 3D using a factorization method. Reconstructed points located within the valid ear region are stored as the ear model. The dataset used consists of video sequences for 48 subjects. Each test model is optimally aligned to the database models using a combination of geometric transformations which result in a minimal partial Hausdorff distance. For the SFS system, the ear structure is recovered by using reflectance and illumination properties of the scene. Shape matching is performed via iterative closest point. Based on our results, we conclude that both structure from motion and shape from shading are viable approaches for 3D ear recognition from video sequences.

A Multimodal Approach for Face and Ear Biometric System

Multi modal biometric system is one of the major areas of study identified with large applications in recognition system. Single modal biometric systems have to challenge with a variety of problems such as noisy data, Intra-class variations, non-universality, spoof attacks, and unacceptable error rates. Some of these limitations can be solved with multi modal biometric systems. The major purpose of the study is to review and analyze the prime works in multimodal biometric system and its efficiency in recognition rate. The proposed framework of the multimodal biometric system using face and ear is given. This paper also discusses the levels of fusion that are possible and understand the types of challenges focused by prior research work in this area.