From Clothing to Identity: Manual and Automatic Soft Biometrics

Abstract

Abstract-Soft biometrics have increasingly attracted research interest and are often considered as major cues for identity, especially in the absence of valid traditional biometrics, as in surveillance. In everyday life, several incidents and forensic scenarios highlight the usefulness and capability of identity information that can be deduced from clothing. Semantic clothing attributes have recently been introduced as a new form of soft biometrics. Although clothing traits can be naturally described and compared by humans for operable and successful use, it is desirable to exploit computer-vision to enrich clothing descriptions with more objective and discriminative information. This allows automatic extraction and semantic description and comparison of visually detectable clothing traits in a manner similar to recognition by eyewitness statements. This study proposes a novel set of soft clothing attributes, described using small groups of high-level semantic labels, and automatically extracted using computer-vision techniques. In this way we can explore the capability of human attributes vis-a-vis those which are inferred automatically by computer-vision. Categorical and comparative soft clothing traits are derived and used for identification/re-identification either to supplement soft body traits or to be used alone. The automatically-and manuallyderived soft clothing biometrics are employed in challenging invariant person retrieval. The experimental results highlight promising potential for use in various applications.

Soft biometrics: a survey

“…face, body including limbs and clothing etc. In the following 9 different recognition experiments [7,9,81,95,97,116,121,165,166] and on 5 different datasets, the complete or subset are annotated using crowdsourcing for evaluation. They all contain balanced gender and ethnicity ratio from varying age group.…”

“…So, the outcome is a very large number of annotations received for distinct features like in [116] and [7]. Important is that these datasets cover almost whole human body [95,97]. That is why, the amount of time and effort utilized to annotate these large-scale datasets is too much higher, despite having qualitative annotation.…”

“…Dataset [179,198] Southampton University [46,165,209] and [97]. These soft traits are from different human body permanent and temporary modalities.…”

Super-Fine Attributes with Crowd Prototyping

“…"is subject A taller or shorter than B?" are more objective, accurate and discriminative than categorical labels for unconstrained subject recognition; from the body [1], [38], face [39] and clothing [40].…”

Efficient Gabor-Based Recognition for Handwritten Arabic-Indic Digits

“…In this research we apply k-NN classifier via Euclidean distance and use it in two modes with k=1 and k=3, based on the same k-NN implementation method used in [29]. Nevertheless, k-NN is used here for classification purpose and to nominate k nearest neighbors of a test feature-vector.…”