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Fatigue-related differences in human facial dimensions based on static images
Triyanti V.a,b, Yassierlia, Iridiastadi H.a
a Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung, 40132, Indonesia
b Department of Industrial Engineering, Atma Jaya Catholic University of Indonesia, Jakarta, 12930, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© Published under licence by IOP Publishing Ltd.Automatic fatigue recognition based on eye and mouth movements has been widely researched and used to detect human fatigue. However, there were only few studies that quantitatively examining fatigue status based on static images. This study was a pilot study that aimed to examine differences in human facial dimension between fresh and fatigue condition, based on photos. 4 photos from 8 subjects were taken, each photo depicted the subject in fresh condition with a neutral expression, fresh condition with a happy expression, fatigue condition with neutral expression, and fatigue condition with happy expression. Each photo was analyzed using Face Reader 7.1 software to detect the coordinates of the points around the eyes and mouth. 10 dimensions around the eyes were calculated for each situation. In neutral expressions, paired t-test with significance value of 0.05 proved that in 8 dimensions, value in fresh conditions were different from ones in fatigue conditions. But these results were not proven in the picture with happy expressions. Although further research is needed, this finding could be a first step for developing the knowledge to detect fatigue based on facial static images.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]human facial dimension,Human fatigue,Pilot studies,Static images,T-tests[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]human facial dimension,Human fatigue,static images[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1757-899X/528/1/012029[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]