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In this paper a hybrid technique is used for determining the face from an image. Face detection is one of the tedious job to achieve with very high accuracy. In this paper we proposed a method that combines two techniques that is Orthogonal Laplacianface (OLPP) and Particle Swarm Optimization (PSO). The formula for the OLPP relies on the Locality Preserving Projection (LPP) formula, which aims at...
Amongst various bio-metric traits, face has been widely accepted by researchers and commercial firms. The facecan be recognized by 2-D and 3-D face recognition techniques. Face recognition has various challenges such as, occlusion, pose variations, illumination variations, and expression variations in probe and gallery faces. In this paper challenges due to illumination variations are addressed using...
Variations in illumination still pose a major constraint in face recognition systems. Though many steps have been taken in this area, it continues to be a challenging field in this domain. We propose a framework to overcome this problem by first classifying the image into dark, normal or shadowed, and then selecting an appropriate filter for the image. This step ensures that there is no loss of features...
Newborn swapping and abduction is a global problem and traditional approaches such as ID bracelets and footprinting do not provide the required level of security. This paper introduces the concept of using face recognition for identifying newborns and presents an automatic face recognition algorithm. The proposed multiresolution algorithm extracts Speeded up robust features and local binary patterns...
Existing face recognition systems have demonstrated success in constrained settings with limited variability in illumination, pose, and expression. However, these incremental improvements are not sufficient to transcend the challenging applications such as identifying missing persons or matching individuals with photo ID. These applications require recognition of face images with aging variations...
In large scale applications, hundreds of new subjects may be regularly enrolled in a biometric system. To account for the variations in data distribution caused by these new enrollments, biometric systems require regular re-training which usually results in a very large computational overhead. This paper formally introduces the concept of online learning in biometrics. We demonstrate its application...
Variations in pose, expression, illumination, aging and disguise are considered as major challenges in face recognition and several techniques have been proposed to address these challenges. Plastic surgery, on the other hand, is considered as an arduous research issue; however, it has not yet been studied either theoretically or experimentally. This paper focuses on analyzing the effect of plastic...
This paper presents a novel formulation of multiclass support vector machine by integrating the concepts of soft labels and granular computing. The proposed multiclass mv-granular soft support vector machine uses soft labels to address the issues due to noisy and incorrectly labeled data, and granular computing to make it adaptable to data distributions both globally and locally. The proposed multiclass...
The performance of score-level fusion algorithms is often affected by conflicting decisions generated by the constituent matchers/classifiers. This paper describes a fusion algorithm that incorporates the likelihood ratio test statistic in a support vector machine (SVM) framework in order to classify match scores originating from multiple matchers. The proposed approach also takes into account the...
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