A Contactless Identification System Based on Hand Shape Features

Abstract

This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies -nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms -have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications.

In-air signature verification system using Leap Motion

“…As explained in section 4, this second experiment focuses on applying different statistical features to evaluate their performance. The evaluated features are usually applied in different works (Bachmann et al, 2015;Bailador et al, 2011;Bernardos, Sánchez, Portillo, Besada, & Casar, 2015;Guerra-Segura et al, 2017;Wu et al, 2009). This experiment is developed using the 7 datasets which offer the best results in the first experiment.…”

Advanced face and gesture recognition for visual HMI

“…For this purpose, a combination of 3D HOOF and HOT are used as input feature vectors to an SVM classier. The solution proposed in[135] uses the raw features provided by the Leap Motion combined with an SVM classication algorithm. In[41], they use the trajectory of the palm to obtain the feature vector combined with an SVM solution for recognizing the palm trajectory describing the shape of a number.Some works have also proposed to complement the skeletal information of the Leap Motion with imagery acquired by other sensors, with the purpose of avoiding to process the highly distorted infrared imagery of the Leap Motion.…”