2,547 research outputs found
A Survey on Ear Biometrics
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers
Greedy Search for Descriptive Spatial Face Features
Facial expression recognition methods use a combination of geometric and
appearance-based features. Spatial features are derived from displacements of
facial landmarks, and carry geometric information. These features are either
selected based on prior knowledge, or dimension-reduced from a large pool. In
this study, we produce a large number of potential spatial features using two
combinations of facial landmarks. Among these, we search for a descriptive
subset of features using sequential forward selection. The chosen feature
subset is used to classify facial expressions in the extended Cohn-Kanade
dataset (CK+), and delivered 88.7% recognition accuracy without using any
appearance-based features.Comment: International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 201
Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries
This paper describes the development of a registration framework for image-guided
solutions to the automation of certain routine neurosurgical procedures. The
registration process aligns the pose of the patient in the preoperative space to that
of the intra-operative space. CT images are used in the pre-operative (planning)
stage, whilst white light (TV camera) images are used to capture the intra-operative
pose. Craniofacial landmarks, rather than artificial markers, are used as the
registration basis for the alignment. To further synergy between the user and the
image-guided system, automated methods for extraction of these landmarks have
been developed. The results obtained from the application of a Polynomial Neural
Network (PNN) classifier based on Gabor features for the detection and localisation
of the selected craniofacial landmarks, namely the ear tragus and eye corners in the
white light modality are presented. The robustness of the classifier to variations in
intensity and noise is analysed. The results show that such a classifier gives good
performance for the extraction of craniofacial landmarks
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