With recent advances in pattern recognition and computer vision, mobile palmprint authentication has become an emerging field to provide better facilities and ubiquitous computing for scientific and commercial communities. To effectively streamline this issue, researchers focus on improving authentication performance by designing deep convolutional neural networks. Despite the high potential of the state-of-the-art methods, the challenges of preprocessing computation cost, lack of training samples for big data application, and discriminative feature optimization remain to be carefully addressed. A deep mobile palmprint verification framework focusing on discriminative feature representation is proposed. To this end, an automatic feature mapping is learned from two well-known deep architectures via an effective weighted loss function. Thereafter, a convolution-based feature fusion block is followed by a surrogate model in the feature-matching phase for palmprint verification. From a practical point of view, our framework is cost-effective and can represent discriminative features with high performance. We demonstrate the effectiveness of our framework and mobile database for palmprint verification task beating the state-of-the-art on standard benchmarks. Moreover, experimental results show that our model outperforms previous ones, especially for the few-shot learning application, achieving equal error rates of 0.0281% and 0.0197% for IIT Delhi Touchless Palmprint Database and Hong Kong PolyU Palmprint databases, respectively. It is notable that all codes are open-source and may be accessed online.

Joint feature fusion and optimization via deep discriminative model for mobile palmprint verification

Biometrics is the one of the most emerging technology in our day to day life. Biometrics is going to be a future of security and applications. Why we need biometrics? The password is not user-friendly, we could not dump all password in our brain. Sometimes we couldn’t remember which application which password we used. To overcome all those problems, Biometrics provides security “You are the password for your application”. Biometrics provides trustworthiness. Both behavioral and physiological characteristics of biometric features recognize the individuality of a person whether the user is genuine or an imposter. Hand biometrics is one of the traditional biometric systems. Generally, hand biometrics can be either captured by contact and contactless-based approach. Hand biometrics comprises of palm textures, knuckle, hand geometry, fingerprints which can be used for recognition. Hand biometrics consist of more uniqueness and individuality of the person has been identified. In this survey paper, we are going to present the working of the hand geometry and palm print technically. The primary objective is to study in-depth of hand biometric system and architecture of hand biometrics. The secondary objective is to literature survey on hand geometry and palmprint. This paper also studies the pre-processing and feature extraction of various systems used in hand and palm print. Finally paper addresses the performance evaluation techniques of hand biometrics.

E. GokulaKrishnan

Papers

A Survey on Multi-feature Hand Biometrics Recognition