RSFS: A soft biometrics-based relative support features set for person verification

Abstract

Generally, biometrics is gaining increased attention due to its application for secure and efficient verification – more specifically at border crossing points. Usually, there are many different types of biometrics associated with human body i.e., intrusive like finger prints etc. and non-intrusive, termed as soft biometrics. In order to make the concept of Smart Borders a reality, the non-intrusive soft biometrics are the baseline technology. One of biggest challenge in soft biometrics based verification is to find a highly related set of features from different modalities of human body – as there is large number such soft biometrics associated with human body. In fact, this is extremely useful to select only those soft biometrics which are supportive to each other and relevant to the problem domain. In our work, we thoroughly investigated one of the largest collection of soft biometrics and developed a multiple non-linear regression based framework for the selection of highly supportive and relevant soft biometrics. We used one of the largest dataset e.g., PETA and its annotation for the evaluation of our proposed model. The accuracy is reported in form of MAE and error distribution graphs for two global soft biometrics i.e., gender and age prediction.

Estimating Anthropometric Soft Biometrics: An Empirical Method

“…The application of categorial methods is limited only to short-term tracking, while comparative methods are feasible where the dataset size is small for image or feature-based retrieval. On the other hand, absolute annotation seems one of the most successful methods for soft biometric feature annotation, only if the output of anthropometric feature estimation is highly accurate on images using computer vision techniques [7]. The human body anthropometric measurements can be estimated from two common modalities of the human body such as extended facial region and body including limbs.…”

Comparison Between Face and Gait Human Recognition Using Enhanced Convolutional Neural Network

“…The human face has various traits that have different levels of distinctiveness (Almudhahka et al, 2026). Usually, the overall accuracy of any soft biometrics system depends upon multiple factors, one of which is attribute correlation which is termed as supportive or non-supportive behavior of attributes towards each other during recognition process (Hassan & Izquierdo, 2022).…”