A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications

The Impact of Color Space and Intensity Normalization to Face Detection Performance

In this study, human face detection have been widely conducted and it is still interesting to be research. In this research, strong impact of color space for face i.e., many and multi faces detection by using YIQ, YCbCr, HSV, HSL, CIELAB, and CIELUV are proposed. In this experiment, intensity normality method in one of the color space channel and tested the faces using Android based have been developed. The faces multi image datasets came from social media, mobile phone and digital camera. In this experiment, the color space YCbCr percentage value with the image initial value detection before processing are 67.15%, 75.00%, and 64.58% have been reached. Then, after the normalization process are 83.21%, 87.12%, and 80.21% have been increased. Furthermore, this study showed that color space of YCbCr have reached improvement percentag

An Exploration into Human–Computer Interaction::Hand Gesture Recognition Management in a Challenging Environment

Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human–computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human–computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system’s algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system’s second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation

VOLUNTARY CONTROL OF BREATHING ACCORDING TO THE BREATHING PATTERN DURING LISTENING TO MUSIC AND NON-CONTACT MEASUREMENT OF HEART RATE AND RESPIRATION

We investigated if listening to songs changes breathing pattern which changes autonomic responses such as heart rate (HR) and heart rate variability (HRV) or change in breathing pattern is a byproduct of listening to songs or change in breathing pattern as well as listening to songs causes changes in autonomic responses. Seven subjects (4 males and 3 females) participated in a pilot study where they listened to two types of songs and used a custom developed biofeedback program to control their breathing pattern to match the one recorded during listening to the songs. Coherencies between EEG, breathing pattern and RR intervals (RRI) were calculated to study the interaction with neural responses. Trends in HRV varied only during listening to songs, suggesting that autonomic response was affected by listening to songs irrespective of control of breathing. Effective coherence during songs while spontaneously breathing was more than during silence and during control of breathing. These results, although preliminary, suggest that listening to songs as well as change in breathing patterns changes the autonomic response but the effect of listening to songs may surpass the effect of changes in breathing. We explored feasibility of using non-contact measurements of HR and breathing rate (BR) by using recently developed Facemesh and other methods for tracking regions of interests from videos of faces of subjects. Performance was better for BR than HR, and over currently used methods. However, refinement of the approach would be needed to get the precision required for detecting subtle changes

A hybrid skin color detection using HSV and YCgCr color space for face detection

Skin detection is a key aspect of many computer vision applications including face detection, person identification, illicit content detection and other related applications. In this paper, a skin detection method is proposed combining two color spaces HSV (Hue, Saturation, Value) and YCgCr (luminance, chrominance in green, chrominance in red). The S, Cg and Cr components are used to form a hybrid SCgCr color space. Skin detection results show that, the proposed method can respond well to different skin color tones with less sensitivity to skin-like background pixels. It is also shown that higher face detection rate can be achieved when applied to face detection problem