In this paper, we propose an effective method for disguised face detection and recognition under the complex background. This method consists of two stages. The first stage determines whether the object is a person. In this stage, we propose the first-dynamic-then-static foreground object detection strategy. This strategy exploits the updated learning-based codebook model for moving object detection and uses the Local Binary Patterns (LBP) + Histogram of Oriented Gradients (HOG) feature-based head-shoulder detection for static target detection. The second stage determines whether the face is disguised and the classes of disguises. Experiments show that our method can detect disguised faces in real time under the complex background and achieve acceptable disguised face recognition rate.
The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores from different biometric sources into a single amalgamated matching score to be used by a classifier for further decision making. Differential Evolution (DE) has been employed to adjust these tunable parameters with the objective being the minimization of the overlapping area of the frequency distributions of genuine and imposter scores in the fused score space, which are estimated by Gaussian kernel density method to achieve higher level of accuracy. Experimental results show that, the proposed method outperforms the conventional score-level fusion rules (sum, product, tanh, exponential) when tested on two databases of 4 modalities (fingerprint, iris, left ear and right ear) of 200 and 516 users and thus confirms the effectiveness of score level fusion. The preliminary results provide adequate motivation towards future research in the line of the application of meta-heuristics in score level fusion.
Because of recent advances in imaging technology, the use of image-based evidences, such as faces and tattoos, is increasing dramatically. Face and tattoo images of prisoners are collected regularly for suspect image database establishment. New biometric traits such as skin marks, androgenic hairs, and blood vessels hidden in color images are getting more attention because they are shown to be useful for criminal and victim identification, especially when their faces and tattoos are neither observable nor available. The current manual approach of collecting images of prisoners is extremely time consuming and does not record these new biometric traits. To address this problem, an unprecedented full-body imaging system is developed. Furthermore, an automatic and systematic routine based on the system for effectively collecting prisoners' images is proposed. This paper concentrates on the system hardware design as well as its image collecting and processing capability. The system has been used to collect and process more than 30,000 infrared and color images from 188 subjects. Its performance is very encouraging.
Vein patterns have been used in commercial biometric systems for many years and are recently considered for criminal authentication. Understanding the similarity between genetically identical vein patterns is important, especially when using them in legal cases involving identical twins. Vein patterns sharing the same Deoxyribonucleic acid (DNA) sequence are generally regarded as vein patterns with maximum similarity. If they are completely distinguishable, it implies that the uniqueness of vein patterns is high. Though the genetic dependence of other biometric traits, including fingerprints, faces, palmprints, and irises, have been studied, genetically identical vein patterns have not been studied systematically. With the help of an automatic vein pattern matching algorithm, this paper analyzes and measures the similarity between genetically identical vein patterns. 234 genetically identical forearm pairs and 204 genetically identical thigh pairs were collected for this study. Experimental results indicate that genetically identical vein patterns have extra similarity, but they are distinguishable.
The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG). Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications. In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual. Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics.
