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All Journal Jurnal Teknik ITS TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JAREE (Journal on Advanced Research in Electrical Engineering) Sewagati

Muhammad Attamimi

Institut Teknologi Sepuluh Nopember

Author-ID : 2453192

Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

Published : 11 Documents Claim Missing Document

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Deep learning based facial expressions recognition system for assisting visually impaired persons Hendra Kusuma; Muhammad Attamimi; Hasby Fahrudin
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

The study of attention estimation for child-robot interaction scenarios Muhammad Attamimi; Takashi Omori
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.

Co-Authors Ahmad Fathur Muhtadin Aldy Helnawan Astria Nur Irfansyah Devy Kuswidiastuti Djoko Purwanto Hasby Fahrudin Kelvin Liusiani Kusuma, Hendra Siswanto Mufid Naufal Baay Muhammad Rivai Muharom, Syahri Riza Agung Ronny Mardiyanto, Ronny Rudy Dikairono Ruth Johana Hutagalung Takashi Omori Takayuki Nagai Takayuki Nagai Totok Mujiono, Totok Trulien Jeremiah Elmer Yunardi, Riky Tri

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