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Deep convolutional neural networks have recently proven extremely effective for difficult face recognition problems in uncontrolled settings. To train such networks, very large training sets are needed with millions of labeled images. For some applications, such as near-infrared (NIR) face recognition, such large training data sets are not publicly available and difficult to collect. In this paper,...
In order to reduce the number of accidents caused by the call when the driver was driving, this paper uses the computer vision technology to dectet the behavior of the driver. Based on the constrained local models (CLM) to detect the characteristic changes of the mouth area, combine the HSV color space and the template matching to detect the hand characteristics to judge whether the driver has the...
Command extraction from human beings becomes easier for a machine if it can analyze the non verbal ways of communication such as emotions. This paper focuses on improving the efficiency of extracting emotion from human facial expression images. The features that were extracted in this experiment were obtained from JAFFE (Japanese Female Facial Expression) database which includes 213 images of different...
This study analyzes the effectiveness of the global (the whole face) and local (regions of eyes, nose, and mouth) features for face recognition. Features describing human faces are encoded in local ternary patterns. The two-class support vector machine is used as the supervised learning algorithm for training recognition models. In the recognition process, recognition modes based on the global features...
Face attributes are interesting due to their detailed description of human faces. Unlike prior researches working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a face image according to a given attribute value. Instead of manipulating the whole image, we propose to learn the corresponding residual image defined...
Speechreading is a notoriously difficult task for humans to perform. In this paper we present an end-to-end model based on a convolutional neural network (CNN) for generating an intelligible acoustic speech signal from silent video frames of a speaking person. The proposed CNN generates sound features for each frame based on its neighboring frames. Waveforms are then synthesized from the learned speech...
This paper addresses the problem of transferring CNNs pre-trained for face recognition to a face attribute prediction task. To transfer an off-the-shelf CNN to a novel task, a typical solution is to fine-tune the network towards the novel task. As demonstrated in the state-of-the-art face attribute prediction approach, fine-tuning the high-level CNN hidden layer by using labeled attribute data leads...
Facial landmark detection is a challenging task with broad applications. Many approaches have been proposed with varying degrees of success. Regression based methods update the facial point positions iteratively. The mean shape or shapes sampled from training set is often used as the initialization, which sometimes may lead to a local minimum in update due to the offset of initial positions and target...
This paper proposes a method of facial expression recognition based on Zernike moments and the minimum classification error (MCE) based hidden Markov model (HMM). In the feature extraction of face, the method of Zernike moments feature extraction based on local feature regions is adopted. First, eyes and mouths are segmented from the facial expression image and Zernike moments feature vectors of eyes...
Convolutional neural networks (CNN) have achieved prominent performance in facial landmark detection in recent years. However, the training of such deep network is non-trivial due to the over-fitting problem caused by the insufficient training data and the diminishing gradients problem occurred in the back-propagation. To address these problems, we propose a multi-task learning framework with supervised...
Performance-driven character animation enables users to create expressive results by performing the desired motion of the character with their face and/or body. However, for cutout animations where continuous motion is combined with discrete artwork replacements, supporting a performance-driven workflow has some unique requirements. To trigger the appropriate artwork replacements, the system must...
Thatcher effect or Thatcher illusion is a phenomenon where it becomes difficult to detect local feature changes in an upside down face, despite identical changes being obvious in an upright face. In the Thatcher illusion, in which the eyes and mouth are inverted relative to the rest of the face, looks grotesque when shown upright but not when inverted. Face double illusion is formed by replicating...
The detection of mixed emotions from facial expressions is explored in this paper, in addition to the conventional categorization into six basic human emotions. A new distance-based normalized similarity index is defined for this purpose involving all the training templates in the normalization. The scale-, rotation- and illumination-invariant Difference Theoretic Texture Features (DTTF) are the image...
In this paper, we propose a novel method for facial parts detection based on Deformable Part Model (DPM). In DPM, the parts are useful regions to detect the face and do not always correspond to the facial parts such as eye, nose and mouth. We model facial parts as a part filter and use annotation to training the position and size. In addition, we discuss the algorithm to deal with the variation of...
In this paper, we present a multi-stage regression-based approach for the 300 Videos in-the-Wild (300-VW) Challenge, which progressively initializes the shape from obvious landmarks with strong semantic meanings, e.g. eyes and mouth corners, to landmarks on face contour, eyebrows and nose bridge which have more challenging features. Compared with initialization based on mean shape and multiple random...
Face image analysis continues as an ongoing challenge in biometrics and image processing due to the state variations of facial elements. In this context, the mouth-state plays a fundamental role because its impact on the perception of facial gestures. Current work on mouth-state detection is mainly focused on the creation of classifiers derived from large training datasets. This technique requires...
This paper deals with the emotion recognition in static images. Facial feature extraction plays a very important role in recognizing a particular emotion in humans. In this paper, the facial expressions in humans .i.e., happy, anger, sad, neutral and disgust, are recognized with the help Support Vector Machine classifier. First, a static image is taken. Then, skin region is extracted from that image...
According to the complex manifestation of human facial expression in realistic environment, occlusion problem has become a new challenge and a hot spot in the field of expression recognition. To make facial expression recognition applied in broader way, the main work is to increase the accuracy under different partial occlusion with feasible robust, which is limited by the information missing and...
In this paper we propose a new method for the detection of action units that relies on a novel region-based face representation and a mid-level decision layer that combines region-specific information. Different from other approaches, we do not represent the face as a regular grid based on the face location alone (holistic representation), nor by using small patches centred at iducial facial point...
Reliable detection and recognition of facial expression from still images in the unconstrained real world situations has many potential applications. Smile detection can be used in many applications include modeling systems for psychological studies on human emotional responses, expression recognition technologies, extending image search capabilities etc. This paper proposes an experimental study...
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