Robot movement controller based on dynamic facial pattern recognition

Abstract

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

Masked face with facial expression recognition based on deep learning

“…Our work focusses on detecting face mask with expression to help in decreasing the spreading of coronavirus. Ghazal and Abdullah [5] a machine learning classifiers construction based on the traditional methods have been used for features extraction, Viola-Jones detector [6], Haar features method for face detection [7]- [9], while human detection is accomplished by a histogram of oriented gradients (HOG) feature extraction method in [10]- [12], facial expressions in [13]- [15], scale-invariant feature transform (SIFT) in [16] and local binary pattern (LBP) are the tools used for object detection and its application [17]. In the recent years, the deep learning-based detector proved an excellent performance in feature extraction [18], A convolutional neural network (CNN), which has a superior feature extraction capability, that make pattern recognition tasks relevant to computer vision more accurate [19].…”

Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

“…This study is depending on pattern recognition as the main algorithm for analysis the signals and extract the hand gestures patterns [19]. PR is composed from many stages as shown in the Figure 6.…”

Performance Comparison of Feature Face Detection Algorithm on The Embedded Platform

“…This technique was developed for systems that have limited resources (hardware and software). Image processing devices in embedded systems are used in real-time systems [44] [45]. The available resources are also very limited so the algorithm design and the use of computational image processing must be more efficient.…”