Kieron Messer

The BANCA database and evaluation protocol

In this paper we describe the acquisition and content of a new large, realistic and challenging multi-modal database intended for training and testing multi-modal verification systems. The BANCA database was captured in four European languages in two modalities (face and voice). For recording, both high and low quality microphones and cameras were used. The subjects were recorded in three different scenarios, controlled, degraded and adverse over a period of three months. In total 208 people were captured, half men and half women. In this paper we also describe a protocol for evaluating verification algorithms on the database. The database will be made available to the research community through http://www.ee.surrey.ac.uk/Research/VSSP/banca.

Illumination Invariant Face Recognition: A Survey

The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. In this paper an extensive and up-to-date survey of the existing techniques to address this problem is presented. This survey covers the passive techniques that attempt to solve the illumination problem by studying the visible light images in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.

Multi-scale local binary pattern histograms for face recognition

A novel discriminative face representation derived by the Linear Discriminant Analysis (LDA) of multi-scale local binary pattern histograms is proposed for face recognition. The face image is first partitioned into several non-overlapping regions. In each region, multi-scale local binary uniform pattern histograms1 are extracted and concatenated into a regional feature. The features are then projected on the LDA space to be used as a discriminative facial descriptor. The method is implemented and tested in face identification on the standard Feret database and in face verification on the XM2VTS database with very promising results.

Face verification competition on the XM2VTS database

In the year 2000 a competition was organised to collect face verification results on an identical, publicly available data set using a standard evaluation protocol. The database used was the Xm2vts database along with the Lausanne protocol [14]. Four different institutions submitted results on the database which were subsequently published in [13]. Three years later, a second contest using the same dataset and protocol was organised as part of AVBPA 2003. This time round seven seperate institutions submitted results to the competition. This paper presents the results of the competition and shows that verification results on this protocol have increased in performance by a factor of 3.

A comparison of photometric normalisation algorithms for face verification

The variation of illumination conditions of an object can produce large changes in tthe image plane, significantl impairing the performance of face verification algorithms. We present a comparison of five photometric normalisation algorithms for use in pre-processing face images for the purpose of verification. The algorithms are tested on various configurations of three contrasting databases, namely the Yale B database, the XM2VTS database and the BANCA database.

Face Authentication Competition on the BANCA Database

This paper details the results of a face verification competition [2] held in conjunction with the First International Conference on Biometric Authentication. The contest was held on the publically available BANCA database [1] according to a defined protocol [6]. Six different verification algorithms from 4 academic and commercial institutions submitted results. Also, a standard set of face recognition software from the internet [3] was used to provide a baseline performance measure.

Quality dependent fusion of intramodal and multimodal biometric experts

We address the problem of score level fusion of intramodal and multimodal experts in the context of biometric identity verification. We investigate the merits of confidence based weighting of component experts. In contrast to the conventional approach where confidence values are derived from scores, we use instead raw measures of biometric data quality to control the influence of each expert on the final fused score. We show that quality based fusion gives better performance than quality free fusion. The use of quality weighted scores as features in the definition of the fusion functions leads to further improvements. We demonstrate that the achievable performance gain is also affected by the choice of fusion architecture. The evaluation of the proposed methodology involves 6 face and one speech verification experts. It is carried out on the XM2VTS data base.

A comparative study of automatic face verification algorithms on the BANCA database

The performance of different face identity verification methods on BANCA database is compared. As part of the comparison, we investigate the effect of representation on different approaches to face verification. Two conventional dimensionality reduction methods, namely the Principal Component Analysis and the Linear Discriminant Analysis are studied as well as the use of the raw image space. The results of the comparison show that when the training set size is limited, a better performance is achieved using Normalised Correlation method in the LDA space while Support Vector Machine classifier is superior when a large enough training set is available. Moreover, the SVM is almost insensitive to the choice of representation. However, a dimensionality reduction can be beneficial if constraints on the size of the template are imposed.

Fusion of multiple experts in multimodal biometric personal identity verification systems

We investigate two trainable methods of classifier fusion in the context of multimodal personal identity verification involving eight experts which exploit voice characteristics and frontal face biometrics. As baseline classifier combination methods, simple fusion rules (Sum and Vote) which do not require any training are used. The results of experiments on the XM2VTS database show that all four combination methods investigated yield improved performance. Trainable fusion strategies do not appear to offer better performance than simple rules.

Face Recognition Using Active Near-IR Illumination.

A new approach to overcome the problem caused by illumination variation in face recognition is proposed in this paper. Active Near-Infrared (Near-IR) illumination projected by a Light Emitting Diode (LED) light source is used to provide a constant illumination. The difference between two face images captured when the LED light is on and off respectively, is the image of a face under just the LED illumination, and is independent of ambient illumination. In preliminary experiments with various ambient illuminations, significantly better results are achieved for both automatic and semi-automatic face recognition experiments on LED illuminated faces than on face images under ambient illuminations.