期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2019
卷号：17
期号：2
页码：973-980
DOI：10.12928/telkomnika.v17i2.11772
出版社：Universitas Ahmad Dahlan
摘要：Visually impaired has the limitation in interacting with another human. They usually use the
sense of hearing and touching their face to recognize human. Face recognition is a technology that can be
used to solve this problem. This paper develops a smart cane function by integrated face recognition
feature on the cane using Haar-Like features and Eigenfaces. This paper proposed a portable, real time,
and wearable product. Raspberry Pi supports portability that affects the delay and computing speed of
face recognition algorithms. Utilization of Raspi camera on the eyes glasses is for wearable purposes.
Voice output provides information on whether the face is caught on camera or not. This prototype works
well during the face detection and recognition process. It needs 3 seconds for one-face recognized in
range 0.25 until 1.5 meters from the camera, until the sound and information are generated. It needs is me
5 second for two faces recognized and 10 seconds for 3 faces recognized by a system in the same range
between face and the camera. The accuracy reaches 91.67% for the up-right position face but for other
position the accuracy is only 18% until 32%.
其他摘要：Visually impaired has the limitation in interacting with another human. They usually use the sense of hearing and touching their face to recognize human. Face recognition is a technology that can be used to solve this problem. This paper develops a smart cane function by integrated face recognition feature on the cane using Haar-Like features and Eigenfaces. This paper proposed a portable, real time, and wearable product. Raspberry Pi supports portability that affects the delay and computing speed of face recognition algorithms. Utilization of Raspi camera on the eyes glasses is for wearable purposes. Voice output provides information on whether the face is caught on camera or not. This prototype works well during the face detection and recognition process. It needs 3 seconds for one-face recognized in range 0.25 until 1.5 meters from the camera, until the sound and information are generated. It needs is me 5 second for two faces recognized and 10 seconds for 3 faces recognized by a system in the same range between face and the camera. The accuracy reaches 91.67% for the up-right position face but for other position the accuracy is only 18% until 32%.
