scholarly journals Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Seyed Muhammad Hossein Mousavi ◽  
S. Younes Mirinezhad

AbstractThis 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.

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fuguang Yao ◽  
Liudong Qiu

Facial expression recognition computer technology can obtain the emotional information of the person through the expression of the person to judge the state and intention of the person. The article proposes a hybrid model that combines a convolutional neural network (CNN) and dense SIFT features. This model is used for facial expression recognition. First, the article builds a CNN model and learns the local features of the eyes, eyebrows, and mouth. Then, the article features are sent to the support vector machine (SVM) multiclassifier to obtain the posterior probabilities of various features. Finally, the output result of the model is decided and fused to obtain the final recognition result. The experimental results show that the improved convolutional neural network structure ER2013 and CK+ data sets’ facial expression recognition rate increases by 0.06% and 2.25%, respectively.


2018 ◽  
Vol 84 ◽  
pp. 251-261 ◽  
Author(s):  
Yuanyuan Liu ◽  
Xiaohui Yuan ◽  
Xi Gong ◽  
Zhong Xie ◽  
Fang Fang ◽  
...  

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