Human metrology for person classification and recognition

Human metrology generally refers to the geometric measurements extracted from humans, such as height, chest circumference or foot length. It provides an important soft biometric that can be used in challenging situations such as human identification at a distance, where hard biometric traits cannot easily be acquired. In this work, we first study the question of predictability and correlation in human metrology, using the tools of entropy. We show that various human metrological features are highly correlated with each other. Thus, partial or available measurements can be used to predict other missing measurements. We then investigate the use of human metrology for the prediction of other soft biometrics, viz. gender and weight. In particular, we consider geometric measurements from the head, and those from the remaining parts of the human body, and propose a copula-based model for their use in predicting gender and weight. For gender prediction, the proposed copula-based model results in a 0.7% misclassification rate using both body and head information, 1.0% using only body information, and 12.2% using only head information on the CAESAR 1D database [1] consisting of 2,369 subjects. For weight prediction, the proposed model gives 0.01 mean absolute error (in the range 0 to 1) using both body and head information, 0.01 using only body information, and 0.07 using only measurements from the head. This leads to the assertion that human body metrology contains enough information for reliable prediction of gender and weight. Furthermore, we investigate the efficacy of the model in practical applications, where metrology data may be missing or severely contaminated by various sources of noise. The proposed copula-based technique is observed to reduce the impact of noise on prediction performance. We then study the question of whether face metrology and its use for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. In this work, metrological features are defined in terms of normalized angle and distance measures, and computed based on a set of landii marks on facial images. The performance of the proposed metrology-based method is compared with that of a state-of-the-art appearance-based method for gender classification. Results are reported on two standard face databases, namely, MUCT [110] and XM2VTS [108] containing 276 and 295 visible spectrum images, respectively. The metrology-based approach resulted in an accuracy of 86.83% on the MUCT database and 82.83% on the XM2VTS database. This was slightly lower than that of the appearance-based method by about 3.8% for the MUCT database and about 5.7% for the XM2VTS database. However, results on the WVUM Multispectral database consisting of 100 near infrared images and 100 multispectral images showed that the metrology-based method outperformed the appearance-based method (87.00% vs. 82.00%). Furthermore, we study the question of person recognition (classification and identification) via whole body metrology. Using CAESAR 1D database as baseline, we simulate intra-class variation with various noise models. The experimental results indicate that given enough number of features, our metrology-based recognition system can have promising performance that is comparable to several recent state-of-the-art recognition systems. We propose a non-parametric feature selection methodology, called adapted k-nearest neighbor estimator, which does not rely on intra-class distribution of the query set. This leads to improved results over other nearest neighbor estimators (as feature selection criteria) for moderate number of features. Finally we quantify the discrimination capability of human metrology. Generally, a biometric-based recognition technique relies on an assumption that the given biometric is unique to an individual. However, the validity of this assumption is not yet generally confirmed for most soft biometrics, such as human metrology. A scientific basis for establishing the uniqueness of human metrology will not only quantify the performance of an automatic recognition system, but will also result in the possible admissibility of metrology-based identification technique in various areas such as the