MultiQ: single sensor-based multi-quality multi-modal large-scale biometric score database and its performance evaluation

Abstract

Single sensor-based multi-modal biometrics is a promising approach that offers simple system construction, low cost, and wide applicability to real situations such as CCTV footage-based criminal investigations. In multi-modal biometrics, fusion at the score-level is a popular and promising approach, and data qualities that affect the matching score of each modality are often incorporated as a quality-dependent score-level fusion framework. This paper presents a very large-scale single sensor-based multi-quality multi-modal biometric score database called MultiQ Score Database version 2 to advance the research into evaluation, comparison, and benchmarking of score-level fusion approaches using both quality-independent and quality-dependent protocols. We extracted gait, head, and height modalities from the OU-ISIR Gait Database and introduce spatial resolution (SR), temporal resolution (TR) and view as quality measures that significantly affect biometric system performance. We considered seven and 10 scaling factors for SR and TR, respectively, with four view variations. We then constructed a database comprising approximately 4 million genuine and 7.5 billion imposter score databases. To evaluate this database, we set two different protocols, and provided a set recognition accuracy for state-of-the-art approaches using protocols for both quality-independent and quality-dependent schemes. This database and the evaluation results will be beneficial for score-level fusion research. Additionally, we provide detailed analysis of the recognition accuracies associated with gait, head, and height modalities in different spatial/temporal resolutions and views. These analyses may be useful in criminal investigation research.

The OU-ISIR Large Population Gait Database with real-life carried object and its performance evaluation

“…Regarding RSVM, it is reported in the literature that RankSVM works better in an identification scenario [24,36] because it focuses more on the relative distance between two classes and considers the probe-dependent rank statistics. However, it did not work well in our setting.…”

“…Compared with traditional biometric features, such as DNA, a fingerprint, face, and iris, gait has many unique advantages. The key advantage is that gait can be used to recognize an individual at a distance from a camera without his/her cooperation, even for a relatively low-resolution image sequence [36] and low frame rate [25]. Therefore, gait has the potential to be applied in many applications, such as access control, surveillance, forensics, and criminal investigations from footage from CCTV cameras installed in a public or private space [4,16,19].…”

Spatio-temporal silhouette sequence reconstruction for gait recognition against occlusion

“…Gait recognition is one of the topics of active interest in the biometric research community because it provides unique advantages over other biometric features, such as the face, iris, and fingerprints. For example, it can be captured without the subject's cooperation at a distance and has discriminative capability from relatively low-resolution image sequences [36]. Recently, gait has been used as a forensic feature, and there has already been a conviction produced by gait analysis [14].…”