Abstract: An image processing device includes a data processing unit that processes data of a face image which is captured to include a face. The data processing unit generates an edge image by filtering the face image to detect an edge in a scanning direction, extracts a sampling value as the information regarding a gradient magnitude and whether the gradient is positive or negative from each of positions in the edge image corresponding to a plurality of points constituting the sampling curve, calculates a likelihood with respect to the sampling curve by setting points having a positive gradient and a negative gradient as likelihood evaluation targets in a first point group and a second point group, and detects a sampling curve having a maximum likelihood as a pupil or an iris among a plurality of sampling curves.

Abstract: Semiconductor devices each include: a semiconductor substrate that contains beta-gallium oxide and has a first conductivity type; a first semiconductor region that contains beta-gallium oxide, has the first conductivity type, and is provided on an upper side of the semiconductor substrate; a second semiconductor region that contains beta-gallium oxide, has a second conductivity type, and is provided on an upper side of a part of the first semiconductor region; and a third semiconductor region that contains beta-gallium oxide, has the first conductivity type, and is provided on an upper side of a part of the second semiconductor region. When the first conductivity type is an n-type and the second conductivity type is a p-type, the second semiconductor region further contains a band gap control element. The band gap control element is selected from a group of boron, aluminum, and indium.

Abstract: An image processing device includes a data processing unit that processes data of a face image which is captured to include a face. The data processing unit generates an edge image by filtering the face image to detect an edge in a scanning direction, extracts a sampling value as the information regarding a gradient magnitude and whether the gradient is positive or negative from each of positions in the edge image corresponding to a plurality of points constituting the sampling curve, calculates a likelihood with respect to the sampling curve by setting points having a positive gradient and a negative gradient as likelihood evaluation targets in a first point group and a second point group, and detects a sampling curve having a maximum likelihood as a pupil or an iris among a plurality of sampling curves.