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In this paper, we propose a pedestrian attribute recognition approach and a CNN-based person re-identification framework enhanced by pedestrian attributes. The knowledge of person attributes can help video surveillance tasks like person re-identification as well as person search, semantic video indexing and retrieval to overcome viewpoint changes with their robustness to the inherent visual appearance...
Recognizing the identities of people in everyday photos is still a very challenging problem for machine vision, due to issues such as non-frontal faces, changes in clothing, location, lighting. Recent studies have shown that rich relational information between people in the same photo can help in recognizing their identities. In this work, we propose to model the relational information between people...
Face recognition system is used for the identification and verification of a face from a video or digital image. In the first phase, Viola Jones algorithm is used to detect and crop face region automatically from image/video frame. The second phase is to recognize the face of a person, in our proposed method Bag of Word technique used to extract features from an image which uses SURF for interest...
There is a need for automatic processing and extracting of meaningful metadata from multimedia information, especially in the audiovisual industry. This higher level information is used in a variety of practices, such as enriching multimedia content with external links, clickable objects and useful related information in general. This paper presents a system for efficient multimedia content analysis...
To improve the accuracy of audio-visual speaker identification, we propose a new approach, which achieves an optimal combination of the different modalities on the score level. We use the i-vector method for the acoustics and the local binary pattern (LBP) for the visual speaker recognition. Regarding the input data of both modalities, multiple confidence measures are utilized to calculate an optimal...
Facial expression recognition in complex environment is one of the difficult tasks of visual recognition in recent years. This paper introduces the visual saliency mechanism and we design automatic searching of the face region in the image. Using the narrow band C-V model to evolve curve, the proposed scheme can obtain the accurate face region. Meanwhile, the SVM will be trained by standard database...
Fine-grained Visual Categorization (FGVC) is an open problem in Computer Vision due to subtle differences between categories. The present paper demonstrates that Collaborative Representation based Classification (CRC) can address this problem successfully. Instead of the traditional discriminative approach of classification, CRC takes a co-operative approach by representing the query image as a weighted...
“Ceci n'est pas une pipe” French for “This is not a pipe”. This is the description painted on the first painting in the figure above. But to most of us, how could this painting is not a pipe, at least not to the great Belgian surrealist artist Rene Magritte. He said that the painting is not a pipe, but rather an image of a pipe. In this paper, we present a study on large-scale classification of fine-art...
Anthropology studies show that genetic features are inherited by children from their parents resulting in visual resemblance between them. This paper presents a novel SIFT flow based genetic Fisher vector feature (SF-GFVF) which enhances the facial genetic features for kinship verification. The proposed SF-GFVF feature is derived by applying a novel similarity enhancement method based on SIFT flow...
In real-word visual applications, distribution mismatch between samples from different domains may significantly degrade classification performance. To improve the generalization capability of classifier across domains, domain adaptation has attracted a lot of interest in computer vision. This work focuses on unsupervised domain adaptation which is still challenging because no labels are available...
In this paper, an asymmetric kernel is proposed for extracting sparse features from two-dimensional visual face images for identity recognition. Essentially, the kernel consists of an inner product of two vectors where one of them has been raised to power terms element-wise. The impact of such a power term is suppression of less influential features where only relevant ones are used for estimation...
Despite their impact on computer vision and face recognition, the inner workings of deep convolutional neural networks (CNNs) have traditionally been regarded as uninterpretable. We demonstrate this to be false by proposing prediction gradients to understand how neural networks encode concepts into individual units. In constrast, existing efforts to understand convolutional nets focus on visualizing...
Due to the ongoing biodiversity crisis, many species including great apes such as chimpanzees or gorillas are threatened and need to be protected. To overcome the catastrophic decline of biodiversity, biologists recently started to use remote cameras for wildlife monitoring. However, the manual analysis of the resulting image and video material is extremely tedious, time consuming, and highly cost...
Grandmother cell is a term in neuroscience to imitate the simplistic notion that the brain has a separate neuron to represent every familiar face, with important properties of sparseness and invariance. This paper proposes a linear regression based classification model for face recognition, which learn a mapping from the training feature vectors to the grandmother-cell-like codes, with one unit corresponding...
The task of fine-grained visual categorization is related to both general object recognition and specialized tasks such as face recognition. Hence, we propose to combine two methods popular for general object recognition and face recognition to build a new model-free system for fine-grained visual categorization. Specifically, we use Local Naive-Bayes Nearest Neighbor as a pre-selection method and...
Investigating that some face regions are possibly more reliable than the others when verifying two face images due to the local abnormal differences caused by the uncontrolled factors in unconstrain environment,we propose a novel face verification algorithm based on pairwise pre-estimation. In our algorithm, we estimate the reliability of a face region by detecting abnormal differences on some key...
Facial Recognition is probably one of the most commonly used biometrie characteristics used by humans for recognition. This is one of the reasons why it has been subject of intense research for the past 30 years or so. In this time a lot of work is being done not only in the development of stable, real time facial recognition system but also in acquiring different modalities of facial imagery for...
While modern research in face recognition has focused on new feature representations, alternate learning methods for fusion of features, most have ignored the issue of unmodeled correlations in face data when combining diverse features such as similar visual regions, attributes, appearance frequency, etc. Conventional wisdom is that by using sufficient data and machine, one can learn the systematic...
Biologically inspired model (BIM) is proven to be an effective feature representation approach for visual object categorization. In BIM, two successive S(simple)-to-C(complex) hierarchical layers are performed to simulate the visual perception process of primate visual cortex. However, the intensive computational cost above C1 layer in BIM extremely limits its application in real-time object recognition...
In the field of gender recognition, face vision information is an important factor. But to achieve strong robustness and high recognition performance, a new framework fusing the fingerprint and face image representation is put forward in this paper. At the stage of image feature representation, "bag of words model" is used to capture the significant features in fingerprint and face image...
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