Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition

Abstract

Automatic gender classification has become a topic of great interest to the visual computing research community in recent times. This is due to the fact that computer-based automatic gender recognition has multiple applications including, but not limited to, face perception, age, ethnicity, identity analysis, video surveillance and smart human computer interaction. In this paper, we discuss a machine learning approach for efficient identification of gender purely from the dynamics of a person's smile. Thus, we show that the complex dynamics of a smile on someone's face bear much relation to the person's gender. To do this, we first formulate a computational framework that captures the dynamic characteristics of a smile. Our dynamic framework measures changes in the face during a smile using a set of spatial features on the overall face, the area of the mouth, the geometric flow around prominent parts of the face and a set of intrinsic features based on the dynamic geometry of the face. This enables us to extract 210 distinct dynamic smile parameters which form as the contributing features for machine learning. For machine classification, we have utilised both the Support Vector Machine and the k-Nearest Neighbour algorithms. To verify the accuracy of our approach, we have tested our algorithms on two databases, namely the CK+ and the MUG, consisting of a total of 109 subjects. As a result, using the k-NN algorithm, along with tenfold cross validation, for example, we achieve an accurate gender classification rate of over 85%. Hence, through the methodology we present here, we establish proof of the existence of strong indicators of gender dimorphism, purely in the dynamics of a person's smile.

Laughter signature, a new approach to gender recognition

“…For instance, the raw face images, 4 both local and low‐level features from the face image 6,28 and face images combined with voice 29‐31 have been used for GR studies. Ugail and Al‐Dahoud 32 as well as Dantcheva and Bremond 33 studied the efficiency of gender identification from dynamic smile parameters. Their works used Support Vector Machine (SVM), K‐Nearest Neighbors, Adaboost, and Bagged Tree as classifiers.…”

Computational Analysis of Smile Weight Distribution across the Face for Accurate Distinction between Genuine and Posed Smiles

“…There exist scientific research too that appear to validate such beliefs or claims. In fact, many traits appear to be encoded in the smile of a person such as the ability to win a fight [1], whether a couple would divorce [2] and even such traits as gender appear to be encoded in the smile [3,4,29].…”