Abstract

This study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.

Highlights

  • Before the appearance of depth images, there were just two dimensions for calculating a color digital image, but depth images or 2.5-Dimentional (2.5-D) images added a third dimension to calculation which led to making 3-Dimentional (3-D) form of objects

  • Each expression is labelled and final matrix is ready for classification using Support Vector Machine (SVM) [38], Multi-Layer Neural Network (MLNN) [50] and Convolutional Neural Network (CNN) [50] algorithms

  • Our proposed Iranians Kinect Face Database (IKFDB) database in both Facial Expression Recognition (FER) and Facial Micro Expressions Recognition (FMER) achieved 90% and 72% recognition accuracy for Support vector machine (SVM), 92% and 76% for MLNN, 95% and 85% for CNN respectively, which is quite promising

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Summary

Introduction

Before the appearance of depth images, there were just two dimensions for calculating a color digital image, but depth images or 2.5-Dimentional (2.5-D) images added a third dimension to calculation which led to making 3-Dimentional (3-D) form of objects. With emergence of depth sensors like Microsoft Kinect [1], working on 3-D applications has gained ease of use. Thanks to its cheap price and high quality, it is widely used in a lot of fields. Academic arena is one of the fields depth image science has had a significant impact on. A simultaneous development in both hardware and software in the field of computer vision is necessary. Are some major applications of depth images: 3-D modeling and reconstruction [2], augmented reality [3], industry [1], medicine [4], Human-Computer Interaction (HCI) [5, 51], Robotics [6] and more. Identity recognition using fingerprints and iris is so precise, but it is limited to recognizing human identity.

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