Fusion of deep-learned and hand-crafted features for cancelable recognition systems
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METHODOLOGIES AND APPLICATION
Fusion of deep-learned and hand-crafted features for cancelable recognition systems Essam Abdellatef1 • Eman M. Omran2 • Randa F. Soliman3 • Nabil A. Ismail4 • Salah Eldin S. E. Abd Elrahman4 Khalid N. Ismail5,6 • Mohamed Rihan7 • Fathi E. Abd El-Samie7,8 • Ayman A. Eisa2
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Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract The recent years have witnessed a dramatic shift in the way of biometric identification, authentication, and security processes. Among the essential challenges that face these processes are the online verification and authentication. These challenges lie in the complexity of such processes, the necessity of the personal real-time identifiable information, and the methodology to capture temporal information. In this paper, we present an integrated biometric recognition method to jointly recognize face, iris, palm print, fingerprint and ear biometrics. The proposed method is based on the integration of the extracted deep-learned features together with the hand-crafted ones by using a fusion network. Also, we propose a novel convolutional neural network (CNN)-based model for deep feature extraction. In addition, several techniques are exploited to extract the hand-crafted features such as histogram of oriented gradients (HOG), oriented rotated brief (ORB), local binary patterns (LBPs), scale-invariant feature transform (SIFT), and speeded-up robust features (SURF). Furthermore, for dimensional consistency between the combined features, the dimensions of the hand-crafted features are reduced using independent component analysis (ICA) or principal component analysis (PCA). The core of this paper is the template protection via a cancelable biometric scheme without significantly affecting the recognition performance. Specifically, we have used the bio-convolving approach to enhance the user’s privacy and ensure the robustness against spoof attacks. Additionally, various CNN hyper-parameters with their impact on the proposed model performance are studied. Our experiments on various datasets revealed that the proposed method achieves 96.69%, 95.59%, 97.34%, 96.11% and 99.22% recognition accuracies for face, iris, fingerprint, palm print and ear recognition, respectively. Keywords Deep learning Feature fusion Cancelable biometrics
1 Introduction
Communicated by V. Loia. & Essam Abdellatef [email protected] Eman M. Omran [email protected] Randa F. Soliman [email protected] Nabil A. Ismail [email protected] Salah Eldin S. E. Abd Elrahman [email protected]
Biometrics achieve a promising performance for the personal verification and authentication in many applications, which include law enforcement, forensics, immigration, border, and access control. Unlike passwords or tokens that Khalid N. Ismail [email protected] Mohamed Rihan [email protected] Fathi E. Abd El-Samie [email protected] Ayman A. Eisa [email protected]
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