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In this paper, we propose an attention-aware deep reinforcement learning (ADRL) method for video face recognition, which aims to discard the misleading and confounding frames and find the focuses of attentions in face videos for person recognition. We formulate the process of finding the attentions of videos as a Markov decision process and train the attention model through a deep reinforcement learning...
We propose AcFR, an active face recognition system that employs a convolutional neural network and acts consistently with human behaviors in common face recognition scenarios. AcFR comprises two main components—a recognition module and a controller module. The recognition module uses a pre-trained VGG-Face net to extract facial image features along with a nearest neighbor identity recognition algorithm...
In this paper, we propose a discriminative keypoint selection-based 3D face recognition method that is superior to prevalent techniques in terms of both computational complexity and performance. We use the average face model (AFM) for face registration to efficiently locate the axis of symmetry in the rotated face mesh and recover a full frontal face from a 3D face model commonly corrupted due to...
Visually challenged are majorly dependent on the Braille language for comprehensive reading of textual documents and their walking sticks they hold everyday for their obstruction identification. Making them virtually visible in an environment and get them workable in a technical organisation this system innovates the technology which provides audio descriptions of their blind surroundings. The design...
Most affect based systems analyse facial expressions for emotion detection, and utilize face detection and recognition methods in order to do effective affect analysis. Recent work has demonstrated the efficacy of deep architectures for face recognition by training as classifiers on voluminous datasets. Some architectures are trained as classifiers, and some directly learn an embedding via a triplet...
Discriminating probabilistic graphical models are reliable tools for a sequence labeling task. Conditional Random Fields (CRFs) are discriminative models which will enable us to label a sequence of input data. Other variations of CRFs have been proposed. Hidden Conditional Random Fields (HCRFs) incorporate hidden states to the CRF model and assign a label for the whole input sequence as the model's...
Unlike many approaches that use detected facial points to infer facial expressions, in this work, we propose an approach in which we jointly tackle the two tasks on a frame basis. After ensuring the consistent face cropping, our framework makes use of geometric- and appearance- based methods for the facial expression recognition, and of cascade regression and local-based methods for the facial point...
Deep face model learned on big dataset surpasses human for face recognition task on difficult unconstrained face dataset. But in practice, we are often lack of resources to learn such a complex model, or we only have very limited training samples (sometimes only one for each class) for a specific face recognition task. In this paper, we address these problems through transferring an already learned...
We present a method to combine the Fisher vector representation and the Deep Convolutional Neural Network (DCNN) features to generate a rerpesentation, called the Fisher vector encoded DCNN (FV-DCNN) features, for unconstrained face verification. One of the key features of our method is that spatial and appearance information are simultaneously processed when learning the Gaussian mixture model to...
The work presents an approach towards facial emotion recognition using face dataset consisting of four classes of emotions (happy, angry, neutral and sad) with different models of deep neural networks and compares their performance. We take the raw pixels values of all images in CMU face images dataset. The pixels values were represented by higher level concepts by feeding them into Restricted Boltz-mann...
Existing facial expression recognition (FER) algorithms aim to extract discriminative features from a face. These discriminative features can be extracted only from the informative regions of a face. In this view, several face models are proposed which are mainly intended to extract geometrical features from a face, and hence these models may not be suitable for extract discriminative texture features...
The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bilinear CNN (B-CNN), which has shown dramatic performance gains on certain fine-grained recognition problems [15]. We apply this new CNN to the challenging new face recognition benchmark, the IARPA Janus Benchmark A (IJB-A)...
Known Face identification quiz is one of common brain and knowledge mapped quiz. This quiz is defined under different complexities levels for different age groups. These facial quiz having the variation in terms of partial face visibility, scattered face, maze hidden face, highly blurred face etc. In this paper, six of the complex facial quiz problems are been resolved using intelligent level driven...
Analyzing social relations through image processing is an active and emerging research topic. Family is the basic unit of a society; recognizing and categorizing family photos is an essential step towards image-based social analysis. In this paper, we propose an approach that leverages a geometric model and an appearance model for family photo detection. The geometric feature captures scene-level...
This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second...
To recognize facial expression from candid, non-posed images, we propose a deep-learning based approach using convolutional neural networks (CNNs). In order to evaluate the performance in real-time candid facial expression recognition, we have created a candid image facial expression (CIFE) dataset, with seven types of expression in more than 10,000 images gathered from the Web. As baselines, two...
Face recognition has been widely used in many application areas such as photo album management and information security. Rapid growth of handheld devices and social networks bring new challenges to face recognition algorithm design and system engineering. To be effective on a handheld device, the face recognition model must be simple and lightweight, and also needs to handle the large variations in...
In this paper, we present a novel approach for recognition of human faces using Markov Random Fields (MRF) and Bayesian models. We examine the relationship between feature vectors in a close proximity system. The feature vectors are coefficients of the 2D Gabor Wavelet Transform (DWGT). The MRF is implemented to match the constraint configurations between the feature vectors. The MRFs posterior probability...
Facial expressions are one of the most important elements for our social interaction. Automatic processing and recognition of facial expressions is hence one of the core areas in computer vision, computer graphics, and social signal processing. Conditional Random Fields (CRFs) and their extensions are widely used for recognizing facial expressions. Most research in this area, however, is done either...
Automatic face recognition in unconstrained environments is a challenging task. To test current trends in face recognition algorithms, we organized an evaluation on face recognition in mobile environment. This paper presents the results of 8 different participants using two verification metrics. Most submitted algorithms rely on one or more of three types of features: local binary patterns, Gabor...
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