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Face recognition and verification is still a challenging problem due to several issues such as pose, facial expression, occlusion, imaging conditions, rotation, size and orientation. This paper addresses the problem of recognizing human faces despite the presence in pose and size variation. To handle these problems, we mainly focus on block size definition. Instead of uniform block we thus propose...
In this paper, it is shown that Local Zernike Moments which is used in object and face recognition applications succesfully, can also used for face-pair matching problem. In this study, instead of using feature vectors produced by LZM directly, we focussed on reducing the dimensions of feature vectors and increasing the performance. In the light of experimental results, a new method called L2ML-YZM...
Glasses detection is one of attractive tasks in image processing since it increases the performance of face recognition systems. In this study, we aimed to detect the glasses on face images automatically. In order to do this, we trained a classifier with Labelled Faces in the Wild Home(LFW) dataset to decide whether a person wear glasses or not on face images. Before classification process, image...
In this paper, a feature combining method which can be used in gender classification has been proposed. This method is based on examinating the importance of the pixel regions on face images. In this study, after the analysing commonly used three feature extraction methods (Local binary patterns, discrete cosine transform, histogram of oriented gradients) dimension reduction is achieved via eliminating...
The performance of a face recognition system is negatively affected by the accessories used on the face. Like many methods, the recognition performance of the Common Vector Approach (CVA) [1] over occluded images is not at the desired level. In this work, we proposed an extension of the CVA, namely the Modular Common Vector Approach (M-CVA), which improves the recognition performance at the occluded...
Many modern computer vision systems combine high dimensional features and linear classifiers to achieve better classification accuracy. However, the excessively long features are often highly redundant; thus dramatically increases the system storage and computational load. This paper presents a novel feature selection algorithm, namely cardinal sparse partial least square algorithm, to address this...
Image level fusion combines an image in different ways with its original version so that the combine image may contain more relevant information than the original one. This paper presents a novel method for face recognition by fusing original and corresponding diagonal images. Two ways of image fusion technique have been performed here. Firstly, we generate diagonal face image from original face image...
This paper presents a hybrid approach to estimate female facial beauty based on Machine Learning techniques. We use a combination of two approaches: Beauty Mask and Facial Proportions, to find the features that constitute Ideal Female facial beauty and thus, develop a female facial beauty scoring system based on the same. The dataset used in this work consists of 30 images being rated by 29 people...
Location and time are two critical aspects of most security-related events, and thus, spatiotemporal data analysis plays a central role in many security-related applications. The human brain has great capabilities of developing invariant representations of objects by taking advantage of both spatial similarity of features of objects/events and their relative timings (temporal information). Trace learning...
The paper studies the problem of making an affine system defined on a polytopic state space reach a prescribed facet of the polytope in finite time without first leaving the polytope. The focus is on solvability by continuous piecewise affine feedback, and we formulate a variant of the problem in which trajectories exit in a monotonic sense. This allows to obtain necessary and sufficient conditions...
Variation in pose is one of the main obstacles confronting researchers in the area of face recognition. In this paper, a novel method is proposed to explicitly tackle this problem. Multi-color uniform local binary pattern (ULBP) is introduced for extracting salient features along with wavelet transform. Learning scheme is adopted to obtain a mapping coefficient vector between face in a pose and frontal...
There have recently been many methods proposed for matching face sequences in the field of face retrieval. However, most of them have proven to be inefficient in large-scale video databases because they frequently require a huge amount of computational cost to obtain a high degree of accuracy. We present an efficient matching method that is based on the face sequences (called face tracks) in large-scale...
Recently it has been shown that the performance of image set matching methods can be improved by clustering set samples into smaller and more coherent groups. Typically, set samples are treated independently during clustering, ie., clustering criteria have not been defined to exploit set characteristics. In this paper we introduce a novel approach to image set clustering by considering the similarities...
Incremental principal component analysis (IPCA) has been of great interest in computer vision and machine learning. In this paper, we introduce a new incremental learning procedure for principal component analysis (PCA). The proposed method can keep an accurate track of the mean of the data, and can deal with a set of new observed data in batch each time in subspace updating. Furthermore, a weighting...
In this study a representation using scale and invariant generic 3D features, for 3D facial models is proposed. These generic feature vectors obtained from descriptive parts of the face like eyes, nose, or nose saddle, are then convolved into a graphical model where a characteristic topology for a 3D facial model representation is achieved. These scale and invariant 3D features are determined by using...
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