Due to their high distinctiveness, robustness to illumination and simple computation, Histogram of Oriented Gradient (HOG) features have attracted much attention and achieved remarkable success in many computer vision tasks. In this paper, an innovative framework for driver drowsiness detection is proposed, where an adaptive descriptor that possesses the virtue of distinctiveness, robustness and compactness is formed from an improved version of HOG features based on binarized histograms of shifted orientations. The final HOG descriptor generated from binarized HOG features is fed to the trained Naïve Bayes (NB) classifier to make the final driver drowsiness determination. Experimental results on the publicly available NTHU-DDD dataset verify that the proposed framework has the potential to be a strong contender for several state-of-the-art baselines, by achieving a competitive detection accuracy of 85.62%, without loss of efficiency or stability.

Early detection of skin cancer through improved techniques and innovative technologies has the greatest potential for significantly reducing both morbidity and mortality associated with this disease. In this paper, an effective framework of a CAD (Computer-Aided Diagnosis) system for melanoma skin cancer is developed mainly by application of an SVM (Support Vector Machine) model on an optimized set of HOG (Histogram of Oriented Gradient) based descriptors of skin lesions. Experimental results obtained by applying the presented methodology on a large, publicly accessible dataset of dermoscopy images demonstrate that the proposed framework is a strong contender for the state-of-the-art alternatives by achieving high levels of sensitivity, specificity, and accuracy (98.21%, 96.43% and 97.32%, respectively), without sacrificing computational soundness.

Amongst all biometric-based personal authentication systems, a fingerprint that gives each person a unique identity is the most commonly used parameter for personal identification. In this paper, we present an automatic fingerprint-based authentication framework by means of fingerprint enhancement, feature extraction, and matching techniques. Initially, a variant of adaptive histogram equalization called CLAHE (contrast limited adaptive histogram equalization) along with a combination of FFT (fast Fourier transform), and Gabor filters are applied to enhance the contrast of fingerprint images. The fingerprint is then authenticated by picking a small amount of information from some local interest points called minutiae point features. These features are extracted from the thinned binary fingerprint image with a hybrid combination of Harris and SURF feature detectors to render significantly improved detection results. For fingerprint matching, the Euclidean distance between the corresponding Harris-SURF feature vectors of two feature points is used as a feature matching similarity measure of two fingerprint images. Moreover, an iterative algorithm called RANSAC (RANdom SAmple Consensus) is applied for fine matching and to automatically eliminate false matches and incorrect match points. Quantitative experimental results achieved on FVC2002 DB1 and FVC2000 DB1 public domain fingerprint databases demonstrate the good performance and feasibility of the proposed framework in terms of achieving average recognition rates of 95% and 92.5% for FVC2002 DB1 and FVC2000 DB1 databases, respectively.

Minutiae feature extraction and matching are not only two crucial tasks for identifying fingerprints, but also play an eminent role as core components of automated fingerprint recognition (AFR) systems, which first focus primarily on the identification and description of the salient minutiae points that impart individuality to each fingerprint and differentiate one fingerprint from another, and then matching their relative placement in a candidate fingerprint and previously stored fingerprint templates. In this paper, an automated minutiae extraction and matching framework is presented for identification and verification purposes, in which an adaptive scale-invariant feature transform (SIFT) detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified and described accurately and quickly. Then, the SIFT descriptors of the local key-points in a given fingerprint are matched with those of the stored templates using a brute force algorithm, by assigning a score for each match based on the Euclidean distance between the SIFT descriptors of the two matched keypoints. Finally, a postprocessing dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while discarding very few correct matches (less than 4%). The experimental evaluations on publicly available low-quality FVC2004 fingerprint datasets demonstrate that the proposed framework delivers comparable or superior performance to several state-of-the-art methods, achieving an average equal error rate (EER) value of 2.01%.

The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discriminative descriptor for skin lesions is derived from local patterns of Gabor-based entropic features. The input skin image is first preprocessed by filtering and histogram equalization to reduce noise and enhance image quality. An automatic thresholding by the optimized formula of Otsu’s method is used for segmenting out lesion regions from the surrounding healthy skin regions. Then, an extensive set of optimized Gabor-based features is computed to characterize segmented skin lesions. Finally, the normalized features are fed into a trained Multilevel Neural Network to classify each pigmented skin lesion in a given dermoscopic image as benign or melanoma. The proposed detection methodology is successfully tested and validated on the public PH2 benchmark dataset using 5-cross-validation, achieving 97.5%, 100% and 96.87% in terms of accuracy, sensitivity and specificity, respectively, which demonstrate competitive performance compared with several recent state-of-the-art methods.