Biometric identification of the human face is a pervasive subject which deals with a wide range of disciplines such as image processing, computer vision, pattern recognition, artificial intelligence, and cognitive psychology. Extracting key face points for developing software and commercial devices of face surgery analysis is one of the most challenging fields in computer image and vision processing. Many studies have developed a variety of techniques to extract facial features from color and gray images. In recent years, using depth information has opened up new approaches to researchers in the field of image processing. Hence, in this study, a statistical method is proposed to extract key nose points from color-depth images (RGB-D) of the face front view. In this study, the Microsoft Kinect sensor is used to produce the face RGB-D images. To assess the capability of the proposed method, this algorithm is applied to 20 RGB-D face images from the database collected in the ICT lab of Sahand University of Technology and promising results are achieved for extracting key points of the face. The results of this study indicated that using the available information in two different color-depth bands could make key points of the face more easily accessible and bring better results and we can conclude that the proposed algorithm provided a promising outcome for extracting the positions of key points.

In biomedical imaging studies, numerous methods have been used to capture human data, mostly by using magnetic resonance imaging (MRI) and computed tomography (CT). However, due to being inexpensive and accessibility of Microsoft Kinect, its usage has been significantly increased in recent years. In this study, we aimed to represent the procedure of data acquisition, which includes a set of depth images from individuals’ back surfaces. The goal of image acquisition is to investigate spinal deformities and landmark detection of the back surface. Traditional imaging systems are challenging, most notably because of ionized beams in the data acquisition process, which has not been solved yet. Indeed, noninvasiveness is the most crucial advantage of our study. Our imaging system was set in a dim laboratory, and the University approved the ethical letter of Medical Sciences before data acquisition. After that, the subjects (total 105; 50 women and 55 men) were recruited, and data images were captured from the back surface. Then, we increased the imaging data size by using the augmentation method to use deep learning methods in future works. Finally, this Dataset leads us to the desired output in our study procedure.

Studying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities that cause physical dysfunction and correspondingly reduce people's quality of life. Radiography is the most common method for detecting these deformities and requires monitoring and follow-up until full treatment. This method frequently exposes the patient to X-rays and ionization. Therefore, cancer risk is increased in the patient and could be riskier for children or pregnant women. To prevent this, several solutions have been proposed using topographic data analysis of the human back surface. The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and practical information about the status of the human postural structure to the physician. In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach - Deep convolution neural network- was used as a model to estimate the location of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed that 86.9% of PDJ and 80% of PCK. According to the results, this study was more effective than other methods with more than thousands of training data.

Studying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities leading to physical dysfunction and correspondingly affecting people's quality of life. Radiography is the most common method for detecting these deformities; however, it frequently exposes the patient to X-rays and ionization and consequently increases cancer risk in patients particularly children and pregnant women.The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and functional information about the status of the human postural structure to the physician. In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach – a deep convolution neural network- was used as a model to estimate the locations of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed 86.9% of PDJ and 80% of PCK that demonstrate more effectiveness compared to other methods.