To deal with severe variation in recording conditions, most biometric systems acquire multiple biometric samples, at the
enrolment stage, for the same person and then extract their individual biometric feature vectors and store them in the
gallery in the form of biometric template(s), labelled with the person's identity. The number of samples/templates and
the choice of the most appropriate templates influence the performance of the system. The desired biometric template(s)
selection technique must aim to control the run time and storage requirements while improving the recognition accuracy
of the biometric system. This paper is devoted to elaborating on and discussing a new two stages approach for biometric
templates selection and update. This approach uses a quality-based clustering, followed by a special criterion for the
selection of an ultimate set of biometric templates from the various clusters. This approach is developed to select
adaptively a specific number of templates for each individual. The number of biometric templates depends mainly on the
performance of each individual (i.e. gallery size should be optimised to meet the needs of each target individual). These
experiments have been conducted on two face image databases and their results will demonstrate the effectiveness of
proposed quality-guided approach.
Existing face recognition schemes are mostly based on extracting biometric feature vectors either from whole face images, or from a
fixed facial region (e.g., eyes, nose, and mouth). Extreme variation in quality conditions between biometric enrolment and verification
stages badly affects the performance of face recognition systems. Such problems have partly motivated several investigations into the
use of partial facial features for face recognition. Nevertheless, partial face recognition is potentially useful in several applications, for
instance, it used in forensics for detectives to identify individuals after some accidents such as fire or explosion. In this paper, we
propose a scheme to fuse the biometric information of partial face images incrementally based on their recognition accuracy (or
discriminative power) ranks. Such fusion scheme uses the optimal ratio of full/partial face images in each different quality condition.
We found that such scheme is also useful for full face images to enhance authentication accuracy significantly. Nevertheless, it
reduces the required storage requirements and processing time of the biometric system. Our experiments show that the required ratio
of full/partial facial images to achieve optimal performance varies from (5%) to (80%) according to the quality conditions whereas the
authentication accuracy improves significantly for low quality biometric samples.
The high intra-class variability of acquired biometric data can be attributed to several factors such as quality of
acquisition sensor (e.g. thermal), environmental (e.g. lighting), behavioural (e.g. change face pose). Such large fuzziness
of biometric data can cause a big difference between an acquired and stored biometric data that will eventually lead to
reduced performance. Many systems store multiple templates in order to account for such variations in the biometric data
during enrolment stage. The number and typicality of these templates are the most important factors that affect system
performance than other factors. In this paper, a novel offline approach is proposed for systematic modelling of intra-class
variability and typicality in biometric data by regularly selecting new templates from a set of available biometric images.
Our proposed technique is a two stage algorithm whereby in the first stage image samples are clustered in terms of their
image quality profile vectors, rather than their biometric feature vectors, and in the second stage a per cluster template is
selected from a small number of samples in each clusters to create an ultimate template sets. These experiments have
been conducted on five face image databases and their results will demonstrate the effectiveness of proposed quality guided approach.
Recent advances in biometric technology have pushed towards more robust and reliable systems. We aim to build
systems that have low recognition errors and are less affected by variation in recording conditions. Recognition errors are
often attributed to the usage of low quality biometric samples. Hence, there is a need to develop new intelligent
techniques and strategies to automatically measure/quantify the quality of biometric image samples and if necessary
restore image quality according to the need of the intended application. In this paper, we present no-reference image
quality measures in the spatial domain that have impact on face recognition. The first is called symmetrical adaptive
local quality index (SALQI) and the second is called middle halve (MH). Also, an adaptive strategy has been developed
to select the best way to restore the image quality, called symmetrical adaptive histogram equalization (SAHE). The
main benefits of using quality measures for adaptive strategy are: (1) avoidance of excessive unnecessary enhancement
procedures that may cause undesired artifacts, and (2) reduced computational complexity which is essential for real time
applications. We test the success of the proposed measures and adaptive approach for a wavelet-based face recognition
system that uses the nearest neighborhood classifier. We shall demonstrate noticeable improvements in the performance
of adaptive face recognition system over the corresponding non-adaptive scheme.