14,268 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
Facial emotion recognition using min-max similarity classifier
Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved in facial emotion
recognition in recent years, the effective and computationally simple feature
selection and classification technique for emotion recognition is still an open
problem. In this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of inter-class pixel
mismatch during classification. The proposed method includes the application of
pixel normalization to remove intensity offsets followed-up with a Min-Max
metric in a nearest neighbor classifier that is capable of suppressing feature
outliers. The results indicate an improvement of recognition performance from
92.85% to 98.57% for the proposed Min-Max classification method when tested on
JAFFE database. The proposed emotion recognition technique outperforms the
existing template matching methods
Automatic method for detection of characteristic areas in thermal face images
The use of thermal images of a selected area of the head in screening systems,
which perform fast and accurate analysis of the temperature distribution of individual areas,
requires the use of profiled image analysis methods. There exist methods for automated face
analysis which are used at airports or train stations and are designed to detect people with
fever. However, they do not enable automatic separation of specific areas of the face. This
paper presents an algorithm for image analysis which enables localization of characteristic
areas of the face in thermograms. The algorithm is resistant to subjects’ variability and also to
changes in the position and orientation of the head. In addition, an attempt was made to
eliminate the impact of background and interference caused by hair and hairline. The algorithm
automatically adjusts its operation parameters to suit the prevailing room conditions.
Compared to previous studies (Marzec et al., J Med Inform Tech 16:151–159, 2010), the set
of thermal images was expanded by 34 images. As a result, the research material was a total of
125 patients’ thermograms performed in the Department of Pediatrics and Child and
Adolescent Neurology in Katowice, Poland. The images were taken interchangeably with
several thermal cameras: AGEMA 590 PAL (sensitivity of 0.1 °C), ThermaCam S65
(sensitivity of 0.08 °C), A310 (sensitivity of 0.05 °C), T335 (sensitivity of 0.05 °C) with a
320×240 pixel optical resolution of detectors, maintaining the principles related to taking
thermal images for medical thermography. In comparison to (Marzec et al., J Med Inform Tech
16:151–159, 2010), the approach presented there has been extended and modified. Based on
the comparison with other methods presented in the literature, it was demonstrated that this
method is more complex as it enables to determine the approximate areas of selected parts of the face including anthropometry. As a result of this comparison, better results were obtained
in terms of localization accuracy of the center of the eye sockets and nostrils, giving an
accuracy of 87 % for the eyes and 93 % for the nostrils
On Person Authentication by Fusing Visual and Thermal Face Biometrics
Recognition algorithms that use data obtained by imaging faces in the thermal spectrum are promising in achieving invariance to extreme illumination changes that are often present in practice. In this paper we analyze the performance of a recently proposed face recognition algorithm that combines visual and thermal modalities by decision level fusion. We examine (i) the effects of the proposed data preprocessing in each domain, (ii) the contribution to improved recognition of different types of features, (iii) the importance of prescription glasses detection, in the context of both 1-to-N and 1-to-1 matching (recognition vs. verification performance). Finally, we discuss the significance of our results and, in particular, identify a number of limitations of the current state-of-the-art and propose promising directions for future research
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