Abstract
In this century, smartphone has become one of the important things that most of the people brings these powerful machines with extensive battery life, qualified built-in cameras, some kinds of sensors and other multifunctional tools that may be useful in everyday routine. Also, the worldwide situation about this time, the issue of facial expression evaluation by a computer is considered as one of the most interesting topics because facial expression identification systems have been applied for many fields, for instance, assessing the customer, as an assistance in health care systems especially for those patients who suffer autism. In this research, an automatic facial expressions identification system for a user is developed as an application to the Android platform. In order to facilitate the application, some existing tools are used to develop with required computations. To work a system fast and stable, the existing SDKs from Affectiva have been decided to apply. The proposed system points out the way to classify the basic emotional conditions, namely anger, joy, sadness, fear, disgust, and surprise for a user.
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References
Magdin M (2017) Real time facial expression recognition using webcam and SDK affectiva. Int J Interact Multimedia Artif Intell
Mehrabian A (2008) Communication without words. In Communication theory, pp 193–200
Keltner D (2009) Born to be good: the science of a meaningful life. WW Norton & Company, New York
Ekman P, Friesen WV (1971) Constants across cultures in the face and emotions. J Personality Soc Psychol 17(2):124–129
Ekman P (1994) Strong evidence for universals in facial expressions: a reply to Russell’s mistaken critique. Psychol Bull 115(2):268–287
Ekman P, Friesen WV (1977) Manual for the facial action coding system. Consulting Psychologists Press
MPEG Video and SNHC (1998) Text of ISO/IEC FDIS 14 496–3: audio. In: Atlantic City MPEG Mtg, Oct 1998, Doc ISO/MPEG N2503
Ekman P et al (1975) Unmasking the face: a guide to recognizing emotions from facial cues
https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework
McDuff D, Kaliouby ER, Senechal T, Amr M, Cohn JF, Picard R (2013) Affectiva-mit facial expression dataset (AM-FED): naturalistic and spontaneous facial expressions collected ‘in-the-wild’. In: Paper presented at the IEEE computer society conference on computer vision and pattern recognition workshops, pp 881–888. https://doi.org/10.1109/CVPRW.2013.130
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Shwe Sin, T., Khin, O. (2022). Facial Expressions Classification on Android Smartphone for a User. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 216. Springer, Singapore. https://doi.org/10.1007/978-981-16-1781-2_21
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DOI: https://doi.org/10.1007/978-981-16-1781-2_21
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