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This paper proposes a psychologically inspired convolutional neural network (PI-CNN) to achieve automatic facial beauty prediction. Different from the previous methods, the PI-CNN is a hierarchical model that facilitates both the facial beauty representation learning and predictor training. Inspired by the recent psychological studies, significant appearance features of facial detail, lighting and...
When using a set of generic head-related transfer functions (HRTFs) for spatial sound rendering, personalisation can be considered to minimise localisation errors. This typically involves tuning the characteristics of the HRTFs or a parametric model according to the listener's anthropometry. However, measuring anthropometric features directly remains a challenge in practical applications, and the...
The performance of local descriptors such as SIFT drops under severe illumination changes. In this paper, we propose a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. Moreover, a robust contrast flipping estimate is proposed based on the...
The sparse representation based classification (SRC) performs not very well for small sample data. A discriminative common vector dictionary based SRC is introduced in this paper to address this issue. The contribution of this paper is that the dictionary of the proposed method is constructed by the discriminative common vector per class. The common vector represents the invariant property of each...
Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poorly on such images. In this paper we present a method for face recognition adapted to real-world conditions that can be trained using very few training...
In recent days, a number of face recognition and authentication mechanisms are developed in the computer vision applications. The human faces may be obstructed by other object that makes the acquisition of fully holistic image processing as a complex task. To overcome this problem, a new partial face recognition system is introduced in this paper. This work includes the preprocessing, face detection,...
Biometric features are widely used in real time applications for unique human identification. Iris is one of the physiological biometric features which are regarded as highly reliable in biometric identification systems. Often iris is combined with other biometric features for robust biometric systems. It is also observed that biometrics is combined with cryptography for stronger security mechanisms...
Biometric is emerging area in the computer science for the secure various systems. Day to day life peoples are preferred to use, robust and highly acceptable security system which can surpass the human errors. Many scientists are engaged to develop a strong biometric system, but there are a lot of challenges in the real time application. It is observed and found that researchers are only working on...
This paper presents a novel Robust Deep Appearance Models (RDAMs) approach to learn the non-linear correlation between shape and texture of face images. In this approach, two crucial components of face images, i.e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively. The RDBM, an alternative form of Robust Boltzmann Machines, can separate...
In recent years, state-of-the-art face recognition performance has improved by using deep convolutional neural networks. One disadvantage of these methods is their need for very large, labeled training datasets as collecting and labeling them can be time consuming and prone to error. In this work we examine the robustness of a convolutional neural network to limited training data and training data...
Facial attributes are emerging soft biometrics that have the potential to reject non-matches, for example, based on mismatching gender. To be usable in stand-alone systems, facial attributes must be extracted from images automatically and reliably. In this paper, we propose a simple yet effective solution for automatic facial attribute extraction by training a deep convolutional neural network (DCNN)...
It has been shown that significant age difference between a probe and gallery face image can decrease the matching accuracy. If the face images can be normalized in age, there can be a huge impact on the face verification accuracy and thus many novel applications such as matching driver's license, passport and visa images with the real person's images can be effectively implemented. Face progression...
Advancement in computer technology has made possible to evoke new video processing applications in field of biometric face detection and recognition. It has wide range of applications in human recognition, human computer interaction (HCI), behavior analysis, teleconferencing and video surveillance. Face is vital part of human anatomy that reflects prominent topographies of a person. Face detection...
The goal of this article is to analyze the assurance of permissible quality indices in an interval system through the construction of the edge route and the use of D-partition method. There were obtained conditions for construction of D-partition domains on one and two edges of one face. On the basis of these conditions the technique for assurance of the permissible degree of robust stability and...
In this paper we evaluate the performance of CNN in regards to face recognition for real world applications. In recent years, many high performance deep neural networks have been proposed to the face recognition world. These deep networks were trained by images provided by the internet, and they commonly are of good quality when facial expression and posture are not particularly complex. However,...
With the growing of available large datasets for evaluation, face detection in recent literature has progressed rapidly. However, little research has been dedicated to develop a face detector robust to all possible variations. To address this problem, novel unconstrained datasets containing faces with more challenging variations are proposed. We notice that some recent face detectors have not been...
3D face recognition holds great promise in achieving robustness to pose, expressions and occlusions. However, 3D face recognition algorithms are still far behind their 2D counterparts due to the lack of large-scale datasets. We present a model based algorithm for 3D face recognition and test its performance by combining two large public datasets of 3D faces. We propose a Fully Convolutional Deep Network...
In order to solve the problems of face image super-resolution, a robust online dictionary learning method based on sparse representation is proposed in this paper. The online dictionary learning algorithms which can be used to train big sample datasets is introduced in the dictionary learning phase to generate better overcomplete dictionaries. Additionally, the classic L2-regularization is replaced...
over the last few years, manifold clustering has attracted considerable interest in high-dimensional data clustering. However achieving accurate clustering results that match user desires and data structure is still an open problem. One way to do so is incorporating additional information that indicate relation between data objects. In this paper we propose a method for constrained clustering that...
We introduce a novel end-to-end real-time pose-robust 3D face tracking framework from RGBD videos, which is capable of tracking head pose and facial actions simultaneously in unconstrained environment without intervention or pre-calibration from a user. In particular, we emphasize tracking the head pose from profile to profile and improving tracking performance in challenging instances, where the...
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