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The new framework proposed in this paper provides an insight into the problem of face authentication (verification) in unconstrained environment. This unconventional method extracts and represents the microstructures and local features of a given face image by greedy approach and sparse code respectively. This gives a stable and discriminative local descriptor for each patch that hinge on the local...
An innovative approach based on local components called Optimal Random Image Component Selection is presented in this paper. Here, features are extracted from the Optimal Random Image Components by Gabor wavelets using greedy approach is proposed. These feature vectors are then down-sampled to some size which is then classified based on minimum distance measure. The design of Gabor filters for facial...
In this paper, we propose a pedestrian analysis solution helpful for adaptive content delivery and interest measurement for outdoor advertisement displays. The proposed system has built-in camera on the top panel of such displays which capture the real time viewers' frames. The captured frames have been analyzed for detection of faces using Viola-Jones algorithm. The detected faces have been processed...
This research work is related to the application of machine vision technique to develop a robust assistive human computer interaction technology for those with physical accessibility problem of controlling mouse and keyboard with hand. Paper's main motif is inferring information about planer movement of the head using a video camera and transforming this motion to the pixel coordinate system of the...
This paper delves into the problem of face recognition using color as an important cue in improving the accuracy of recognition. To perform recognition of color images, we use the characteristics of a 3D color tensor to generate a color LDA subspace, which in turn can be used to recognize a new probe image. To test the accuracy of our methodology, we computed the recognition rate across two color...
This paper presents a memetic algorithm based new approach to feature selection in face recognition. In this work, principal component analysis (PCA) has been used for dimensionality reduction/feature extraction and memetic algorithms have been applied for selection of features in face recognition application. ORL face database has been used for performing the experiments. The results indicate that...
Face recognition and expression analysis is one of the most challenging research areas in the field of computer vision. Even though face exhibits different facial expressions, which can be instantly recognized by human eyes, it is very difficult for a computer to extract and use the information content from these expressions. In this paper we present a method to analyze facial expression by focusing...
Pattern recognition problem rely on the features inherent in the pattern of images. Face detection and recognition is one of the challenging research areas in the field of computer vision. In this paper, we present a method to identify skin pixels from still and video images using skin color. Face regions are identified from this skin pixel region. Facial features such as eyes, nose and mouth are...
We address the problem of tracking and recognizing faces in real-world, noisy videos. We track faces using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting. However, adaptive appearance trackers often suffer from drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, our tracker introduces visual constraints...
This paper examines the problem of extracting low-dimensional manifold structure given millions of high-dimensional face images. Specifically, we address the computational challenges of nonlinear dimensionality reduction via Isomap and Laplacian Eigenmaps, using a graph containing about 18 million nodes and 65 million edges. Since most manifold learning techniques rely on spectral decomposition, we...
In many vision problems, instead of having fully labeled training data it is easier to obtain the input in small groups, where the data in each group is constrained to be from the same class but the actual class label is not known. Such constraints give rise to partial equivalence relations. The absence of class labels prevents the use of standard discriminative methods in this scenario. On the other...
In this paper we describe the architecture, implementation, and performance of a face verification system that continually verifies the presence of a logged-in user at a computer console. It maintains a sliding window of about ten seconds of verification data points and uses them as input to a Bayesian framework to compute a probability that the logged-in user is still present at the console. If the...
In this paper we describe the theory, architecture, implementation, and performance of a multimodal passive biometric verification system that continually verifies the presence/participation of a logged-in user. We assume that the user logged in using strong authentication prior to the starting of the continuous verification process. While the implementation described in the paper combines a digital...
Face recognition has always been a fascinating research area. It has drawn the attention of many researchers because of its various potential applications such as security systems, entertainment, criminal identification etc. Many supervised and unsupervised learning techniques have been reported so far. Principal component analysis (PCA) is a classical and successful method for face recognition. Self...
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