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Facial micro-expression refers to split-second muscle changes in the face, indicating that a person is either consciously or unconsciously suppressing their true emotions and even mental health. Therefore, micro-expression recognition attracts increasing research efforts in both fields of psychology and computer vision. Existing research on micro-expression recognition has mainly used hand-crafted...
Face sketch synthesis plays an important role in both law enforcement and digital entertainment. The existing methods for sketch synthesis always suffer from noising and blurring effect. To resolve these problems, a nonsubsampled Shearlet transform (NSST) based detail enhancement strategy is proposed. The exemplar-based method is firstly adopted to synthesize the primary sketch, then the final sketch...
In this paper, we introduce seven emotions and positive and negative emotion recognition methods using facial images and the development of apps based on the method. In previous researches, they used the deep-learning technology to generate models with emotion-based facial expressions to recognized emotions. There are existing apps that express six emotions, but not seven emotions and positive and...
Face sketch to digital image matching is an important challenge of face recognition that involves matching across different domains. Current research efforts have primarily focused on extracting domain invariant representations or learning a mapping from one domain to the other. In this research, we propose a novel transform learning based approach termed as DeepTransformer, which learns a transformation...
In the interest of recent accomplishments in the development of deep convolutional neural networks (CNNs) for face detection and recognition tasks, a new deep learning based face recognition attendance system is proposed in this paper. The entire process of developing a face recognition model is described in detail. This model is composed of several essential steps developed using today's most advanced...
The human face is an important biometric quantity which can be used to access a user-based system. As human face images can easily be obtained via mobile cameras and social networks, user-based access systems should be robust against spoof face attacks. In other words, a reliable face-based access system can determine both the identity and the liveness of the input face. To this end, various feature-based...
Multimodal biometrie systems seek to alleviate some of the limitations of unimodal biometrie systems by combining multiple pieces of evidence of the same person in the deeision-making process. In this paper, a novel multimodal biometric identification system is proposed based on fusing the results obtained from both the face and the left and right irises using deep learning approaches. Firstly, the...
Ethnicity is one of the most salient clues to face identity. Analysis of ethnicity-specific facial data is a challenging problem and predominantly carried out using computer-based algorithms. Current published literature focusses on the use of frontal face images. We addressed the challenge of binary (British Pakistani or other ethnicity) ethnicity classification using profile facial images. The proposed...
Nowadays, with the increasing use of biometric data, it is expected that systems work robustly and they can give successful results against difficult situations and forgery. In face recognition systems, variables such as direction of light, facial expression and reflection makes identification difficult. With biometric fusion, both safe and high performance results can be achieved. In this work, Eurocom...
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup...
Over the last few years, increased interest has arisen with respect to age-related tasks in the Computer Vision community. As a result, several "in-the-wild" databases annotated with respect to the age attribute became available in the literature. Nevertheless, one major drawback of these databases is that they are semi-automatically collected and annotated and thus they contain noisy labels...
Abundance and availability of video capture devices, such as mobile phones and surveillance cameras, have instigated research in video face recognition, which is highly pertinent in law enforcement applications. While the current approaches have reported high accuracies at equal error rates, performance at lower false accept rates requires significant improvement. In this paper, we propose a novel...
In this paper, we present a new face recognition algorithm based on weighted deep face learning. Our proposed method composes of two steps: face detection and face feature extraction. The aim of face detection is to find an accurate face position. The face alignment is then applied by finding the facial landmarks in the face rectangle. With the help of face alignment the error rate of face recognition...
Linear Discriminant Regression Classification (LDRC) is an effective method developed in the recent years on aim of providing enhancement to the accuracy of Face Recognition (FR) based systems. The visible general problems in face recognition are fraudulent faces and the factors affecting recognition accuracy such as noise, diversions in the angle, poses and expression. These problems are the main...
Biometric systems may be used to create a remote access model on devices, ensure personal data protection, personalize and facilitate the access security. Biometric systems are generally used to increase the security level in addition to the previous authentication methods and they seen as a good solution. Biometry occupies an important place between the areas of daily life of the machine learning...
Proposed algorithm is a face recognition algorithm from video using Generalized mean Deep Learning Neural Network. Generalized mean provides fast convergence of the feature set and Deep learning neural network is enhanced using wavelet transform as it improves the classification efficiency of the neural network. The performance of the proposed algorithm is evaluated on PaSC and Youtube dataset. The...
In this paper, we proposed an optimized Sparse Deep Learning Network (SDLN) model for Face Recognition (FR). A key contribution of this work is to learn feature coding of human face with a SDLN based on local structured Sparse Representation (SR). In traditional sparse FR methods, different poses and expressions of training samples could have great influence on the recognition results. We consider...
Facial expression has made significant progress in recent years with many commercial systems are available for real-world applications. It gains strong interest to implement a facial expression system on a portable device such as tablet and smart phone device using the camera already integrated in the devices. It is very common to see face recognition phone unlocking app in new smart phones which...
Deep Learning has becoming a popular and effective way to address a large set of issues. In particular, in computer vision, it has been exploited to get satisfying recognition performance in unconstrained conditions. However, this wild race towards even better performance in extreme conditions has overshadowed an important step i.e. the assessment of the impact of this new methodology on traditional...
Although automated classification of soft biometric traits in terms of gender, ethnicity and age is a well-studied problem with a history of more than three decades, it is still far from being considered a solved problem for the case of difficult exposure conditions, such as during night-time, in environments with unconstrained lighting, or at large distances from the camera. In this paper, we investigate...
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