An Extensive Review on Spectral Imaging in Biometric Systems: Challenges and Advancements

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images

DBC based Face Recognition using DWT

The applications using face biometric has proved its reliability in last decade. In this paper, we propose DBC based Face Recognition using DWT (DBC- FR) model. The Poly-U Near Infra Red (NIR) database images are scanned and cropped to get only the face part in pre-processing. The face part is resized to 100*100 and DWT is applied to derive LL, LH, HL and HH subbands. The LL subband of size 50*50 is converted into 100 cells with 5*5 dimention of each cell. The Directional Binary Code (DBC) is applied on each 5*5 cell to derive 100 features. The Euclidian distance measure is used to compare the features of test image and database images. The proposed algorithm render better percentage recognition rate compared to the existing algorithm.Comment: 15 pages,9 figures, 4 table

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Evaluating feature extractors and dimension reduction methods for near infrared face recognition systems

This study evaluates the performance of global and local feature extractors as well as dimension reduction methods in NIR domain. Zernike moments (ZMs), Independent Component Analysis (ICA), Radon Transform + Discrete Cosine Transform (RDCT), Radon Transform + Discrete Wavelet Transform (RDWT) are employed as global feature extractors and Local Binary Pattern (LBP), Gabor Wavelets (GW), Discrete Wavelet Transform (DWT) and Undecimated Discrete Wavelet Transform (UDWT) are used as local feature extractors. For evaluation of dimension reduction methods Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis + Principal Component Analysis (Fisherface), Kernel Fisher Discriminant Analysis (KFD) and Spectral Regression Discriminant Analysis (SRDA) are used. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that ZMs as a global feature extractor, UDWT as a local feature extractor and SRDA as a dimension reduction method have superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments

Finger Vein Template Protection with Directional Bloom Filter

Biometrics has become a widely accepted solution for secure user authentication. However, the use of biometric traits raises serious concerns about the protection of personal data and privacy. Traditional biometric systems are vulnerable to attacks due to the storage of original biometric data in the system. Because biometric data cannot be changed once it has been compromised, the use of a biometric system is limited by the security of its template. To protect biometric templates, this paper proposes the use of directional bloom filters as a cancellable biometric approach to transform the biometric data into a non-invertible template for user authentication purposes. Recently, Bloom filter has been used for template protection due to its efficiency with small template size, alignment invariance, and irreversibility. Directional Bloom Filter improves on the original bloom filter. It generates hash vectors with directional subblocks rather than only a single-column subblock in the original bloom filter. Besides, we make use of multiple fingers to generate a biometric template, which is termed multi-instance biometrics. It helps to improve the performance of the method by providing more information through the use of multiple fingers. The proposed method is tested on three public datasets and achieves an equal error rate (EER) as low as 5.28% in the stolen or constant key scenario. Analysis shows that the proposed method meets the four properties of biometric template protection. Doi: 10.28991/HIJ-2023-04-02-013 Full Text: PD

Local feature extraction based facial emotion recognition: a survey

Notwithstanding the recent technological advancement, the identification of facial and emotional expressions is still one of the greatest challenges scientists have ever faced. Generally, the human face is identified as a composition made up of textures arranged in micro-patterns. Currently, there has been a tremendous increase in the use of local binary pattern based texture algorithms which have invariably been identified to being essential in the completion of a variety of tasks and in the extraction of essential attributes from an image. Over the years, lots of LBP variants have been literally reviewed. However, what is left is a thorough and comprehensive analysis of their independent performance. This research work aims at filling this gap by performing a large-scale performance evaluation of 46 recent state-of-the-art LBP variants for facial expression recognition. Extensive experimental results on the well-known challenging and benchmark KDEF, JAFFE, CK and MUG databases taken under different facial expression conditions, indicate that a number of evaluated state-of-the-art LBP-like methods achieve promising results, which are better or competitive than several recent state-of-the-art facial recognition systems. Recognition rates of 100%, 98.57%, 95.92% and 100% have been reached for CK, JAFFE, KDEF and MUG databases, respectively