13,047 research outputs found
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which
includes a wide range of commercial and law enforcement applications. Hence it
is unsurprising that it continues to be one of the most active research areas
of computer vision. Even after over three decades of intense research, the
state-of-the-art in face recognition continues to improve, benefitting from
advances in a range of different research fields such as image processing,
pattern recognition, computer graphics, and physiology. Systems based on
visible spectrum images, the most researched face recognition modality, have
reached a significant level of maturity with some practical success. However,
they continue to face challenges in the presence of illumination, pose and
expression changes, as well as facial disguises, all of which can significantly
decrease recognition accuracy. Amongst various approaches which have been
proposed in an attempt to overcome these limitations, the use of infrared (IR)
imaging has emerged as a particularly promising research direction. This paper
presents a comprehensive and timely review of the literature on this subject.
Our key contributions are: (i) a summary of the inherent properties of infrared
imaging which makes this modality promising in the context of face recognition,
(ii) a systematic review of the most influential approaches, with a focus on
emerging common trends as well as key differences between alternative
methodologies, (iii) a description of the main databases of infrared facial
images available to the researcher, and lastly (iv) a discussion of the most
promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap
with arXiv:1306.160
Inner Eye Canthus Localization for Human Body Temperature Screening
In this paper, we propose an automatic approach for localizing the inner eye
canthus in thermal face images. We first coarsely detect 5 facial keypoints
corresponding to the center of the eyes, the nosetip and the ears. Then we
compute a sparse 2D-3D points correspondence using a 3D Morphable Face Model
(3DMM). This correspondence is used to project the entire 3D face onto the
image, and subsequently locate the inner eye canthus. Detecting this location
allows to obtain the most precise body temperature measurement for a person
using a thermal camera. We evaluated the approach on a thermal face dataset
provided with manually annotated landmarks. However, such manual annotations
are normally conceived to identify facial parts such as eyes, nose and mouth,
and are not specifically tailored for localizing the eye canthus region. As
additional contribution, we enrich the original dataset by using the annotated
landmarks to deform and project the 3DMM onto the images. Then, by manually
selecting a small region corresponding to the eye canthus, we enrich the
dataset with additional annotations. By using the manual landmarks, we ensure
the correctness of the 3DMM projection, which can be used as ground-truth for
future evaluations. Moreover, we supply the dataset with the 3D head poses and
per-point visibility masks for detecting self-occlusions. The data will be
publicly released
Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm
In this paper, a thermal infra red face recognition system for human
identification and verification using blood perfusion data and back propagation
feed forward neural network is proposed. The system consists of three steps. At
the very first step face region is cropped from the colour 24-bit input images.
Secondly face features are extracted from the croped region, which will be
taken as the input of the back propagation feed forward neural network in the
third step and classification and recognition is carried out. The proposed
approaches are tested on a number of human thermal infra red face images
created at our own laboratory. Experimental results reveal the higher degree
performanceComment: 7 pages, Conference. arXiv admin note: substantial text overlap with
arXiv:1309.1000, arXiv:1309.0999, arXiv:1309.100
Facial Component Detection in Thermal Imagery
This paper studies the problem of detecting facial components in thermal imagery (specifically eyes, nostrils and mouth). One of the immediate goals is to enable the automatic registration of facial thermal images. The detection of eyes and nostrils is performed using Haar features and the GentleBoost algorithm, which are shown to provide superior detection rates. The detection of the mouth is based on the detections of the eyes and the nostrils and is performed using measures of entropy and self similarity. The results show that reliable facial component detection is feasible using this methodology, getting a correct detection rate for both eyes and nostrils of 0.8. A correct eyes and nostrils detection enables a correct detection of the mouth in 65% of closed-mouth test images and in 73% of open-mouth test images
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