1,911 research outputs found
Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap
Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap)
Ensemble of Hankel Matrices for Face Emotion Recognition
In this paper, a face emotion is considered as the result of the composition
of multiple concurrent signals, each corresponding to the movements of a
specific facial muscle. These concurrent signals are represented by means of a
set of multi-scale appearance features that might be correlated with one or
more concurrent signals. The extraction of these appearance features from a
sequence of face images yields to a set of time series. This paper proposes to
use the dynamics regulating each appearance feature time series to recognize
among different face emotions. To this purpose, an ensemble of Hankel matrices
corresponding to the extracted time series is used for emotion classification
within a framework that combines nearest neighbor and a majority vote schema.
Experimental results on a public available dataset shows that the adopted
representation is promising and yields state-of-the-art accuracy in emotion
classification.Comment: Paper to appear in Proc. of ICIAP 2015. arXiv admin note: text
overlap with arXiv:1506.0500
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS
Face recognition has been a long standing problem in computer vision. General
face recognition is challenging because of large appearance variability due to
factors including pose, ambient lighting, expression, size of the face, age, and distance
from the camera, etc. There are very accurate techniques to perform face
recognition in controlled environments, especially when large numbers of samples
are available for each face (individual). However, face identification under uncontrolled(
unconstrained) environments or with limited training data is still an unsolved
problem. There are two face recognition tasks: face identification (who is who in
a probe face set, given a gallery face set) and face verification (same or not, given
two faces). In this work, we study both face identification and verification in unconstrained
environments.
Firstly, we propose a face verification framework that combines Partial Least
Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a
face with a large feature set combining shape, texture and color information. PLS
regression is applied to perform multi-channel feature weighting on this large feature
set. Finally the PLS regression is used to compute the similarity score of an image
pair by One-Shot learning (using a fixed negative set).
Secondly, we study face identification with image sets, where the gallery and
probe are sets of face images of an individual. We model a face set by its covariance
matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an
efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive
a kernel function that explicitly maps the covariance matrix from the Riemannian
manifold to Euclidean space. Then, discriminative learning is performed on the
COV manifold: the learning aims to maximize the between-class COV distance and
minimize the within-class COV distance.
Sparse representation and dictionary learning have been widely used in face
recognition, especially when large numbers of samples are available for each face
(individual). Sparse coding is promising since it provides a more stable and discriminative
face representation. In the last part of our work, we explore sparse
coding and dictionary learning for face verification application. More specifically,
in one approach, we apply sparse representations to face verification in two ways
via a fix reference set as dictionary. In the other approach, we propose a dictionary
learning framework with explicit pairwise constraints, which unifies the discriminative
dictionary learning for pair matching (face verification) and classification (face
recognition) problems
A Novel Approach to Face Recognition using Image Segmentation based on SPCA-KNN Method
In this paper we propose a novel method for face recognition using hybrid SPCA-KNN (SIFT-PCA-KNN) approach. The proposed method consists of three parts. The first part is based on preprocessing face images using Graph Based algorithm and SIFT (Scale Invariant Feature Transform) descriptor. Graph Based topology is used for matching two face images. In the second part eigen values and eigen vectors are extracted from each input face images. The goal is to extract the important information from the face data, to represent it as a set of new orthogonal variables called principal components. In the final part a nearest neighbor classifier is designed for classifying the face images based on the SPCA-KNN algorithm. The algorithm has been tested on 100 different subjects (15 images for each class). The experimental result shows that the proposed method has a positive effect on overall face recognition performance and outperforms other examined methods
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