A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique

Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images

Despite the successful applications of probabilistic collaborative representation classification (PCRC) in pattern classification, it still suffers from two challenges when being applied on hyperspectral images (HSIs) classification: 1) ineffective feature extraction in HSIs under noisy situation; and 2) lack of prior information for HSIs classification. To tackle the first problem existed in PCRC, we impose the sparse representation to PCRC, i.e., to replace the 2-norm with 1-norm for effective feature extraction under noisy condition. In order to utilize the prior information in HSIs, we first introduce the Euclidean distance (ED) between the training samples and the testing samples for the PCRC to improve the performance of PCRC. Then, we bring the coordinate information (CI) of the HSIs into the proposed model, which finally leads to the proposed locality regularized robust PCRC (LRR-PCRC). Experimental results show the proposed LRR-PCRC outperformed PCRC and other state-of-the-art pattern recognition and machine learning algorithms

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