Modification on Brightness Enhancement for Simple Thresholding in Eyelid Area Measurement

Indrabayu, -; Tacok, Rina Anriani; Areni, Intan Sari

doi:10.1145/3121138.3121197

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…Figure 6 shows the bounding box section to be enlarged. Equation (4)-(7) can be used to increase the size of a bounding box (Indrabayu and Areni, 2017 Where: a = The distance from the leftmost border of the frame to the bounding box's left margin b = The distance from the topmost border of the frame to the top margin of the bounding box c = The length of the bounding box d = The width of the bounding box Figure 7 shows the example of the output frame obtained from the cropping process.…”

Section: Face Detectionmentioning

confidence: 99%

Gender-Based Customer Counting System Using Computer Vision for Retail Stores

Areni

Safitri

Indrabayu

et al. 2020

Journal of Computer Science

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The development of modern retail business is gradually getting faster, increasing the level of competition among retailers. The retailers are changing their business strategies to acquire new customers, maintain customer loyalty and improve customer service. One way to indicate the good market performance of a retail shop is to know the number and details of visitors based on gender. The number of visitors in retail shops can be observed by installing CCTV camera. In the existing shop system, CCTV used just for monitoring activities in the retail shop. Therefore, the data from the CCTV camera can be used to calculate the number of visitors based on gender automatically by utilizing computer vision. This study designed a system to count the number of female visitors through video data. The data acquisition process involved 89 people, 41 women and 48 men. The data are stored in .AVI video file format with a resolution of 7201280 pixels. This system can be divided into three main stages, which are face detection using the Viola-Jones method, feature extraction using Gabor Filter 2D and classification using Support Vector Machine (SVM) method. The result of the study showed that the system can count the number of female and non-female visitors with an accuracy rate of 96.52%. The system performance will be improved by using another feature extraction and classification methods.

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Section: Face Detectionmentioning

confidence: 99%

Gender-Based Customer Counting System Using Computer Vision for Retail Stores

Areni

Safitri

Indrabayu

et al. 2020

Journal of Computer Science

Self Cite

View full text Add to dashboard Cite

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An indoor video-based for single face identification among people

Indrabayu

Areni

Arafah³

et al. 2019

J. Phys.: Conf. Ser.

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The purpose of this research is to build a system that can detect certain face among several people. The system works using face recognition in searching suspects from a database. 30 images and 9 videos are used for training and testing. In the training process carried out several stages: (1) pre-processing of face images, (2) extraction of facial image training features using the Histogram of Oriented Gradient (HOG) method, (3) face classification using the SVM method. There are several steps for testing process i.e.: (1) video frame extraction, (2) cascade classifier method with the Local Binary Pattern (LBP) feature descriptor, (3) HOG for extracting facial features detected in the frames, and (4) SVM as the face classifier. The processed video data is 1920x1080 pixel resolution which has been recorded using a CCTV camera which is mounted on a wall with height and slope angle of 2.5 meters and 60 degrees. There are two goals implemented in this research. The first goal is to find the highest accuracy from several testing of frame sampling by setting the best threshold value. The second goal is to find the lowest processing time.

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