Soft Biometrics for Surveillance: An Overview

Abstract

sp005Biometrics is the science of automatically recognizing people based on physical or behavioral characteristics such as face, fingerprint, iris, hand, voice, gait, and signature. More recently, the use of soft biometric traits has been proposed to improve the performance of traditional biometric systems and allow identification based on human descriptions. Soft biometric traits include characteristics such as height, weight, body geometry, scars, marks, and tattoos (SMT), gender, etc. These traits offer several advantages over traditional biometric techniques. Soft biometric traits can be typically described using human understandable labels and measurements, allowing for retrieval and recognition solely based on verbal descriptions. Unlike many primary biometric traits, soft biometrics can be obtained at a distance without subject cooperation and from low quality video footage, making them ideal for use in surveillance applications. This chapter will introduce the current state of the art in the emerging field of soft biometrics.

Human identification using facial comparative descriptions

“…Soft biometric features [12] are characteristics which people can naturally describe. This new form of biometrics has allowed individuals to be automatically identified from criminal databases based on bodily descriptions [11].…”

Analysing comparative soft biometrics from crowdsourced annotations

“…Soft biometrics originally described subjects in an absolute semantic space, using categorical labels [37,6,35]. More recent works move towards the use of comparative measurements, able to predict relative attribute strengths of faces and natural scenes [31] and texture [26].…”

“…By collectively reviewing the most significant, prevalent and stable traits from [24,35,37,39], the final soft biometric trait lexicon of 2 global and 10 body soft traits is deduced, seen in Table 1. Ethnicity was excluded as a global soft trait, as Lucas et al argue against separating by ethnicity, stating that it is often misinterpreted when describing low quality images [23].…”

“…The study deduces the 13 most reliably interpreted body 'scales', measured on a 5-point scale. These 13 body traits have in turn been assessed for categorical annotation variance [37], stability and discriminative power at-a-distance [39] and imputation accuracy [35].…”

Impact of Facial Cosmetics on Automatic Gender and Age Estimation Algorithms

“…Thus, it is essential to understand the limitations of these software in the presence of facial makeup. Thirdly, due to the use of such software in surveillance applications [Reid et al, 2013], anonymous customized advertisement systems 6 and image retrieval systems [Bekios-Calfa et al, 2011], it is imperative that they account for the presence of makeup if indeed they are vulnerable to it. Fourthly, gender and age have been proposed as soft biometric traits in automated biometric systems [Jain et al, 2004].…”