On Using High-Definition Body Worn Cameras for Face Recognition from a Distance

Communications and Multimedia Security

2013

Abstract. In recent years, the emergence of the new paradigm of compressive sensing (CS) has led to the development of innovative image/signal processing and analysis tools that can be exploited to efficiently deal with serious challenges in pattern recognitions. This paper is concerned with the use of CS tools and dictionaries for face recognition, and in particular when dealing with uncontrolled conditions, e.g. faces captured at a distance in surveillance scenarios or in post-rioting forensic, whereby the images are severely degraded/blurred and of low-resolution. We present the results of our recent investigations 1 into the construction of over-complete dictionaries that recover super-resolved face images from any input low-resolution degraded face image. These results demonstrate that non-adaptive image-independent implicitly designed dictionaries that guarantee the recovery of sparse signals achieve face recognition accuracy levels and yield significant recognition rates that are as good as if not better than those achieved by a recently proposed image-based learnt dictionaries. We shall also show that a variety of random dictionaries known to satisfy the Restricted Isometry Property (RIP), achieve similar accuracy rates, and thereby removing the need for training images. The high quality of the super-resolved images provides great potential for forensics and crime/terrorism fighting.

Section: Fig 4 (Continued)mentioning

confidence: 85%

Face Recognition from Degraded Images – Super Resolution Approach by Non-adaptive Image-Independent Compressive Sensing Dictionaries

Communications and Multimedia Security

2013

“…The UBHSD video database [19] contains 160 videos of 20 subjects recorded in two sessions. It includes videos captured in indoor and outdoor locations; two video recordings -one high-definition (HD) and one standard-definition (SD) -of a subject were captured at each location.…”

Section: Experiments Protocol and Datasetsmentioning

confidence: 99%

Construction of dictionaries to reconstruct high-resolution images for face recognition

Al-Hassan

2013 International Conference on Biometrics (ICB)

Sellahewa

2013

Self Cite

“…The UBHSD video database contains 160 videos of 20 subjects [4]. The videos of each subject were recorded in two sessions with a gap of at least two days between the recording sessions.…”

Section: Ubhsd Databasesmentioning

confidence: 99%

“…The images captured at a distance are usually of lower resolution, which leads to low recognition accuracy. The use of high-definition videos to overcome the problem of low-resolution in face recognition at a distance has recently been investigated by Al-Obaydy and Sellahewa, [4]. Here we usesuper resolution methods on the UBHSD video database to reconstruct a HR image from the lowresolution images (i.e.…”

Section: Ubhsd Databasementioning

confidence: 99%

“…edge-directed based and non-local) [3] can be used. Recently, the use of high definition cameras [4] and example-based approaches to super-resolution (e.g. the compressive sensing dictionary based) [5] have been investigated as an alternative to interpolation for improved resolution in relation to face recognition in unconstrained environments.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing face recognition at a distance using super resolution

Al-Hassan

Proceedings of the on Multimedia and Security

Sellahewa

2012

Self Cite

The characteristics of surveillance video generally include lowresolution images and blurred images. Decreases in image resolution leads to loss of high frequency facial components, which is expected to adversely affect recognition rates. Super resolution (SR) is a technique used to generate a higher resolution image from a given low-resolution, degraded image. In this paper, we investigate two existing super-resolution methods to reconstruct a high resolution from a single/ multiple lowresolution images. The first method is based on pairs of high and low quality dictionaries in the spatial domain and the other method is the so called Back-Project Iterative Interpolation for super resolution. Recent works have used SR as a pre-processing step to overcome the problem of low-resolution images in face recognition. However, super resolution reconstruction process being ill-posed results in visual artifacts that can be visually distracting to humans and/or effect machine feature extraction and face recognition algorithms. In this paper, we propose an alternative super resolution scheme based on dictionaries in high frequency wavelet subbands. The proposed method is evaluated on database of high and low-resolution images-Extended Yale B database and UBHSD database-and for face recognition at a distance. We shall demonstrate that the proposed approach at level 3 DWT resolution has superior performance in comparison to the other methods.