Multi-spectral face recognition: Identification of people in difficult environments

Bourlai, Thirimachos; Čukić, Bojan

doi:10.1109/isi.2012.6284307

Cited by 54 publications

(26 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Texture Based: Local Binary Patters (LBP) and Local Ternary Patterns (LTP) methods considered to be standard texture based face recognition methods. [36][37][38] These methods are used to get the appearance and texture information and is invariant to change in illumination conditions. It is highly discriminative, efficient method and perform well for the FR systems.…”

Section: Face Recognition Experimentsmentioning

confidence: 99%

See 1 more Smart Citation

Can we match ultraviolet face images against their visible counterparts?

2015

Self Cite

View full text Add to dashboard Cite

In law enforcement and security applications, the acquisition of face images is critical in producing key trace evidence for the successful identification of potential threats. However, face recognition (FR) for face images captured using different camera sensors, and under variable illumination conditions, and expressions is very challenging. In this paper, we investigate the advantages and limitations of the heterogeneous problem of matching ultra violet (from 100 nm to 400 nm in wavelength) or UV, face images against their visible (VIS) counterparts, when all face images are captured under controlled conditions.The contributions of our work are three-fold; (i) We used a camera sensor designed with the capability to acquire UV images at short-ranges, and generated a dual-band (VIS and UV) database that is composed of multiple, full frontal, face images of 50 subjects. Two sessions were collected that span over the period of 2 months. (ii) For each dataset, we determined which set of face image pre-processing algorithms are more suitable for face matching, and, finally, (iii) we determined which FR algorithm better matches cross-band face images, resulting in high rank-1 identification rates. Experimental results show that our cross spectral matching (the heterogeneous problem, where gallery and probe sets consist of face images acquired in different spectral bands) algorithms achieve sufficient identification performance. However, we also conclude that the problem under study, is very challenging, and it requires further investigation to address real-world law enforcement or military applications. To the best of our knowledge, this is first time in the open literature the problem of cross-spectral matching of UV against VIS band face images is being investigated.

show abstract

Section: Face Recognition Experimentsmentioning

confidence: 99%

“…39 It can filter out noise using the uniform patterns. 36,37 The LBP operator works as a local 3×3 neighborhood around each pixel, thresholding the pixels in the neighborhood at the location of the central pixel. This creates a binary pattern as a local image descriptor.…”

Section: Face Recognition Experimentsmentioning

confidence: 99%

Can we match ultraviolet face images against their visible counterparts?

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…2. The captured images could be in controlled or uncontrolled conditions (changes in illumination direction and stand-off distances) which could in turn, affect the matching results [23]. 3.…”

Section: Spectral Matchingmentioning

confidence: 99%

An extensive review on spectral imaging in biometric systems: Challenges & advancements

Munir¹,

Khan

2019

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

Spectral imaging has recently gained traction for face recognition in biometric systems. We investigate the merits of spectral imaging for face recognition and the current challenges that hamper the widespread deployment of spectral sensors for face recognition. The reliability of conventional face recognition systems operating in the visible range is compromised by illumination changes, pose variations and spoof attacks. Recent works have reaped the benefits of spectral imaging to counter these limitations in surveillance activities (defence, airport security checks, etc.). However, the implementation of this technology for biometrics, is still in its infancy due to multiple reasons. We present an overview of the existing work in the domain of spectral imaging for face recognition, different types of modalities and their assessment, availability of public databases for sake of reproducible research as well as evaluation of algorithms, and recent advancements in the field, such as, the use of deep learning-based methods for recognizing faces from spectral images.

show abstract

“…Multispectral and hyper-spectral (multiple wavelengths within a band) image understanding is an important capability of a biometrics system, not only in terms of recognition performance [5][6][7][8][9][10] but also in terms of operational efficiency. For example, it is impractical for a forensic tool operator to manually annotate thousands to millions of eye centers before he/she can further apply any set of FR preprocessing, feature extraction, and matching algorithms.…”

Section: Introductionmentioning

confidence: 99%

On designing an unconstrained tri-band pupil detection system for human identification

Whitelam

Bourlai

2015

Machine Vision and Applications

Self Cite

View full text Add to dashboard Cite

The majority of facial recognition systems depend on the accurate location of both the left and right eye centers in an effort to geometrically normalize the face images available in a database under study. In this paper, we propose a novel pupil detection algorithmic approach that automatically and efficiently locates the eye centers of face images captured using visible and infrared sensors when operating under challenging conditions. Our proposed approach includes the usage of long-range and night-time face images captured in the infrared (IR) band under active illumination. It also efficiently deals with partial face obstruction (subjects wearing eye glasses) as well as face pose and illumination variation. Our scenario-adaptable methodological approach involves a number of algorithmic steps, including (i) situation classification (to automatically determine the acquisition scenario each input face image is coming from), (ii) generation and usage of 2D normalized correlation coefficients, (iii) prediction of eye localization, (iv) computation of summation range filters for accurate pupil detection, and an (v) eye glasses classifier on facial images using support vector machines (to determine when subjects are wearing glasses or not). Our proposed approach is compared against state-of-the-art academic and commercial eye detection algorithms, including the (a) Viola and Jones Adaboost method, (b) one of the latest academic eye detection algorithms proposed by Valenti and Gevers (IEEE TPAMI), and (c) the eye detection approach that is available as part of the G8 commercial face recognition software B Cameron Whitelam (package provided by L1 systems). Experimental results demonstrate that our proposed approach outperforms all other approaches when applied on various challenging face datasets, including IR face images captured behind tinted glass or at long ranges up to about 350 feet away, day or night. We also show the benefits of our approach to face normalization and, as a result, face recognition improvement performance in terms of rank-1 identification rates. This is an important achievement that has practical value for forensic tool operators who may have to manually localize the eye centers of all face images available in a dataset, before further face image preprocessing and face-based matching algorithms can be applied.

show abstract

Multi-spectral face recognition: Identification of people in difficult environments

Cited by 54 publications

References 16 publications

Can we match ultraviolet face images against their visible counterparts?

Can we match ultraviolet face images against their visible counterparts?

An extensive review on spectral imaging in biometric systems: Challenges & advancements

On designing an unconstrained tri-band pupil detection system for human identification

Contact Info

Product

Resources

About