Faces In Different Poses Research Articles

FaceFilter: Face Identification with Deep Learning and Filter Algorithm

Although significant advances have been made recently in the field of face recognition, these have some limitations, especially when faces are in different poses or have different levels of illumination, or when the face is blurred. In this study, we present a system that can directly identify an individual under all conditions by extracting the most important features and using them to identify a person. Our method uses a deep convolutional network that is trained to extract the most important features. A filter is then used to select the most significant of these features by finding features greater than zero, storing their indices, and comparing the features of other identities with the same indices as the original image. Finally, the selected features of each identity in the dataset are subtracted from features of the original image to find the minimum number that refers to that identity. This method gives good results, as we only extract the most important features using the filter to recognize the face in different poses. We achieve state-of-the-art face recognition performance using only half of the 128 bytes per face. The system has an accuracy of 99.7% on the Labeled Faces in the Wild dataset and 94.02% on YouTube Faces DB.

A visual attention based ROI detection method for facial expression recognition

Abstract In this paper, an eleven-layered Convolutional Neural Network with Visual Attention is proposed for facial expression recognition. The network is composed of three components. First, local convolutional features of faces are extracted by a stack of ten convolutional layers. Second, the regions of interest are automatically determined according to these local features by the embedded attention model. Third, the local features in these regions are aggregated and used to infer the emotional label. These three components are integrated into a single network which can be trained in an end-to-end scheme. Extensive experiments on four kinds of data (namely aligned frontal faces, faces in different poses, aligned unconstrained faces, and grouped unconstrained faces) prove that the proposed method can improve the accuracy and obtain good visualization. The visualization shows that the learned regions of interest are partly consistent with the locations of emotion specific Action Units. This founding confirms the interpretation of Facial Action Coding System and Emotional Facial Action Coding System from a machine learning perspective.

Pose invariant face recognition: 3D model from single photo

Face recognition is widely studied in the literature for its possibilities in surveillance and security. In this paper, we report a novel algorithm for the identification task. This technique is based on an optimized 3D modeling allowing to reconstruct faces in different poses from a limited number of references (i.e. one image by class/person). Particularly, we propose to use an active shape model to detect a set of keypoints on the face necessary to deform our synthetic model with our optimized finite element method. Indeed, in order to improve our deformation, we propose a regularization by distances on graph. To perform the identification we use the VanderLugt correlator well know to effectively address this task. On the other hand we add a difference of Gaussian filtering step to highlight the edges and a description step based on the local binary patterns. The experiments are performed on the PHPID database enhanced with our 3D reconstructed faces of each person with an azimuth and an elevation ranging from −30° to +30°. The obtained results prove the robustness of our new method with 88.76% of good identification when the classic 2D approach (based on the VLC) obtains just 44.97%.

Neural Network-Based Approach for Face Recognition Across Varying Pose

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

Compositional Dictionaries for Domain Adaptive Face Recognition.

We present a dictionary learning approach to compensate for the transformation of faces due to the changes in view point, illumination, resolution, and so on. The key idea of our approach is to force domain-invariant sparse coding, i.e., designing a consistent sparse representation of the same face in different domains. In this way, the classifiers trained on the sparse codes in the source domain consisting of frontal faces can be applied to the target domain (consisting of faces in different poses, illumination conditions, and so on) without much loss in recognition accuracy. The approach is to first learn a domain base dictionary, and then describe each domain shift (identity, pose, and illumination) using a sparse representation over the base dictionary. The dictionary adapted to each domain is expressed as the sparse linear combinations of the base dictionary. In the context of face recognition, with the proposed compositional dictionary approach, a face image can be decomposed into sparse representations for a given subject, pose, and illumination. This approach has three advantages. First, the extracted sparse representation for a subject is consistent across domains, and enables pose and illumination insensitive face recognition. Second, sparse representations for pose and illumination can be subsequently used to estimate the pose and illumination condition of a face image. Last, by composing sparse representations for the subject and the different domains, we can also perform pose alignment and illumination normalization. Extensive experiments using two public face data sets are presented to demonstrate the effectiveness of the proposed approach for face recognition.

Maximal likelihood correspondence estimation for face recognition across pose.

Due to the misalignment of image features, the performance of many conventional face recognition methods degrades considerably in across pose scenario. To address this problem, many image matching-based methods are proposed to estimate semantic correspondence between faces in different poses. In this paper, we aim to solve two critical problems in previous image matching-based correspondence learning methods: 1) fail to fully exploit face specific structure information in correspondence estimation and 2) fail to learn personalized correspondence for each probe image. To this end, we first build a model, termed as morphable displacement field (MDF), to encode face specific structure information of semantic correspondence from a set of real samples of correspondences calculated from 3D face models. Then, we propose a maximal likelihood correspondence estimation (MLCE) method to learn personalized correspondence based on maximal likelihood frontal face assumption. After obtaining the semantic correspondence encoded in the learned displacement, we can synthesize virtual frontal images of the profile faces for subsequent recognition. Using linear discriminant analysis method with pixel-intensity features, state-of-the-art performance is achieved on three multipose benchmarks, i.e., CMU-PIE, FERET, and MultiPIE databases. Owe to the rational MDF regularization and the usage of novel maximal likelihood objective, the proposed MLCE method can reliably learn correspondence between faces in different poses even in complex wild environment, i.e., labeled face in the wild database.

The perception of faces in different poses by 1‐month‐olds

An experiment is described in which the ability of 1‐month‐old infants to recognize their mothers visually was explored with the live faces of mother and stranger presented in three different poses. The en face and half‐profile poses resulted in significant visual preferences for the mother being demonstrated, but the profile pose did not. The temporal stability of the observed memory is discussed along with potential methodological artifacts.