Robot movement controller based on dynamic facial pattern recognition

Published on May 1, 2021in Indonesian Journal of Electrical Engineering and Computer Science
· DOI :10.11591/IJEECS.V22.I2.PP733-743
Deris Stiawan10
Estimated H-index: 10
(Sriwijaya University)
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).
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Oct 1, 2018 in ICEE (International Conference on Electrical Engineering)
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This paper presents the fusion techniques for detecting and tracking the face. The proposed method combines the Viola-Jones method, the CamShift tracking, and the Kalman Filter tracking. The objective is to increase the face detection rate, while reduce the computation cost. The proposed method is implemented on a low cost embedded system based-on the Raspberry Pi module. The experimental results show that the average detection rate of 98.3% is achieved, and it is superior compared to the existi...
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#2Maurizio GiordanoH-Index: 11
Last. Mariacarla StaffaH-Index: 13
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Object tracking is a challenging problem in many computer vision applications, which go from robotics to surveillance systems. When applied to real world conditions, tracking methods found in the literature compete in solving some inherent difficulties of object segmentation and movement prediction, such as camouflage, occlusions, dynamic background, brightness, color and shape changes. To address some of these issues, we propose a general framework for object tracking by exploiting well-known s...
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#2Chong Xie (SAU: Shenyang Aerospace University)H-Index: 1
Last. Yanna Zhang (SAU: Shenyang Aerospace University)H-Index: 1
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To solve the problems of lean, occlusion, and unbalance exposure, a new strategy of facial expression recognition based on template matching is proposed in the paper. The strategy firstly obtains the characteristics of half-facial expressions by using Gabor wavelet, and then calculates the picture's Euclidean distance with the standard library; at last, classify expressions by using the method of K nearest neighbor. Experiments show that the strategy can improve the rate of facial expression rec...
#1S. Charles Brubaker (Georgia Institute of Technology)H-Index: 6
#2Jianxin Wu (Georgia Institute of Technology)H-Index: 51
Last. James M. Rehg (Georgia Institute of Technology)H-Index: 77
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Cascades of boosted ensembles have become popular in the object detection community following their highly successful introduction in the face detector of Viola and Jones. Since then, researchers have sought to improve upon the original approach by incorporating new methods along a variety of axes (e.g. alternative boosting methods, feature sets, etc.). Nevertheless, key decisions about how many hypotheses to include in an ensemble and the appropriate balance of detection and false positive rate...
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