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In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach. Keywords--Face recognition (FR), Steerable pyramid (SP), local Binary Pattern (LBP)., I. Introduction IN the last two decades, several studies have proposed to deploy multi-resolution feature extraction algorithms in face recognition. Among multi-resolution algorithms, the most popular are Discrete Wavelet transform [...]

In the last decade, mobile devices become a storage place for highly sensitive information such as authentication credentials, bank account information, etc. However, smartphones are more likely to be lost or stolen than desktops or even laptops, putting user data at a high risk of being compromised. Malware and legitimate vulnerable applications can also be a threat to data disclosure. Therefore, this work focuses on preserving the confidentiality of the information stored in mobile devices from unauthorized access and specifically in the Android platform using a fuzzy vault-based key binding algorithm. The proposed approach aims to conceal the cryptographic key with the user's fingerprint such that only the genuine user will be authorized to access the secret key. [ABSTRACT FROM AUTHOR]

Biometric template disclosure starts gaining an important concern in deploying practical biometric authentication systems, where an assailant compromises the database for illegitimate access. To protect biometric templates from disclosure attacks, biometric authentication systems should meet these four requirements: security, diversity, revocability, and performance. Different methods have been suggested in the literature such as feature transformation techniques and biometric cryptosystems. However, no single method could satisfy the four requirements, giving rise to the deployment of hybrid mechanisms. In this context, the current paper proposes a hybrid system for multimodal biometric template protection to provide robustness against template database attacks. Herein, a secure sketch method is first applied to secure the fingerprint modality. Subsequently, a Dual-Tree Complex Wavelet Transform Discrete Cosine Transform (DTCWT-DCT) based watermarking is employed to entrench the fingerprint sketch into the face image. However, a 3D chaotic-map-based encryption method is employed to protect the watermarked facial image in order to offer an added security level. The experimentation performed using the ORL face database and three Fingerprint Verification Competition (FVC) fingerprint databases showed the approach's efficiency in withstanding standard digital image watermarking attacks, brute force attacks, and information leakage. Moreover, the results revealed that the approach achieves high performance, and satisfies diversity and revocability requirements. [ABSTRACT FROM AUTHOR]

There are a number of issues related to the development of biometric authentication systems, such as privacy breach, consequential security and biometric template storage. Thus, the current paper aims to address these issues through the hybrid approach of watermarking with biometric encryption. A multimodal biometric template protection approach with fusion at score level using fingerprint and face templates is proposed. The proposed approach includes two basic stages, enrollment stage and verification stage. During the enrollment stage, discrete wavelet transform (DWT) is applied on the face images to embed the fingerprint features into different directional sub-bands. Watermark embedding and extraction are done by quantizing the mean values of the wavelet coefficients. Subsequently, the inverse DWT is applied to obtain the watermarked image. Following this, a unique token is assigned for each genuine user and a hyper-chaotic map is used to produce a key stream in order to encrypt a watermarked image using block-cipher. The experimentation results indicate the efficiency of the proposed approach in term of achieving a reasonable error rate of 3.87%. [ABSTRACT FROM AUTHOR]