A Survey on Face Detection and Person Re-identification

Vidhyalakshmi, M. K.; Poovammal, E.

doi:10.1007/978-81-322-2734-2_29

Cited by 1 publication

(2 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to tackle this critical issue, the research activity focused on the analysis of human perception with the aim of describing mathematically the processes that lead to the identification of a person [10] and then to implement in FR algorithms able to operate in uncontrolled environments [11]. The main principle of these algorithms is to focus on the analysis of a set of biometric features rather than on the analysis of the full 3D shape of a face.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metrological characterization of 3D biometric face recognition systems in actual operating conditions

et al. 2017

View full text Add to dashboard Cite

<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

show abstract

Section: Introductionmentioning

confidence: 99%

“…In addition, the final classification result is uncertain, up to a point that it may be impossible to find a perfect matching between images of the same person acquired in different moments [11]. Hence, there is a clear risk in accepting the generic classification results without a proper analysis of the uncertainty [15], [16].…”

Section: Introductionmentioning

confidence: 99%