Face Detection and Recognition in Color Images under Matlab

Maia, Deise Santana; Trindade, Roque Mendes Prado

doi:10.14257/ijsip.2016.9.2.02

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…As shown in Fig. 3 [34], the RGB color space is an additive color space, in which red, green and blue lights are added together in various ways to reproduce a broad array of colors. The space sets an RGB value for each pixel of the image to represent the color, and each parameter (RGB) defines the intensity of the color as an integer between 0 and 255.…”

Section: Parallel Design and Implementation Of Fcm A Generating Point Cloud Datamentioning

confidence: 99%

A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data

Liu

et al. 2019

IEEE Access

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With the explosive growth of image big data in the agriculture field, image segmentation algorithms are confronted with unprecedented challenges. As one of the most important images segmentation technologies, the fuzzy c-means (FCMs) algorithm has been widely used in the field of agricultural image segmentation as it provides simple computation and high-quality segmentation. However, due to its large amount of computation, the sequential FCM algorithm is too slow to finish the segmentation task within an acceptable time. This paper proposes a parallel FCM segmentation algorithm based on the distributed memory computing platform Apache Spark for agricultural image big data. The input image is first converted from the RGB color space to the lab color space and generates point cloud data. Then, point cloud data are partitioned and stored in different computing nodes, in which the membership degrees of pixel points to different cluster centers are calculated and the cluster centers are updated iteratively in a data-parallel form until the stopping condition is satisfied. Finally, point cloud data are restored after clustering for reconstructing the segmented image. On the Spark platform, the performance of the parallel FCMs algorithm is evaluated and reaches an average speedup of 12.54 on ten computing nodes. The experimental results show that the Spark-based parallel FCMs algorithm can obtain a significant increase in speedup, and the agricultural image testing set delivers a better performance improvement of 128% than the Hadoopbased approach. This paper indicates that the Spark-based parallel FCM algorithm provides faster speed of segmentation for agricultural image big data and has better scale-up and size-up rates.

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Section: Parallel Design and Implementation Of Fcm A Generating Point Cloud Datamentioning

confidence: 99%

A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data

Liu

et al. 2019

IEEE Access

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“…The key advantage of this approach is its highspeed processing power and low computational requirements, both in terms of speed and memory usage [5]. D. Maia et al explain the application of face detection and recognition algorithms in colour images under MATLAB [6]. Additionally, C. Zhang et al explores three common methods for face dection using PCA, LDA, and EBGM approaches.…”

Section: Introductionmentioning

confidence: 99%

Automatic Attendance Recording System Using Facial Recognition

Kumara

Tahmasebi

Sethu

2021

Malaysian J. Sci. Adv. Tech.

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The student attendance is collected either manually or digitally through an RFID card. Recently, some of the institutions adopted the use of dynamic QR codes for attendance monitoring. However, these systems are challenging to maintain and time-consuming. The student can easily manipulate signatures for attendance monitoring. Moreover, the QR code system requires the application on both the lecturer and student sides. The lecturer needs to set up the QR code while using the mobile app to use the system. Therefore, it is time-consuming and sacrifices several minutes at the beginning of each class. The student also sometimes has difficulty in using the app leading to manual data insertion. Therefore, this project aims to solve this issue by using the facial recognition system on a lecturer’s laptop to collect the attendances. This project requires the use of a webcam and MATLAB to perform facial image recognition. This work utilizes face detection algorithm in the MATLAB image processing toolbox to build a system that will detect and recognize the frontal faces of students in a classroom. Firstly, several images of students will be recorded in a database. Then, a GUI-based application automatically identifies a face and matches it with the database created. Lecturers will use the GUI on their laptops, and students will show their faces to the web camera for their attendance to be taken. The system was tested successfully where student face was successfully recognized and recorded in the attendance monitoring system. Only registered user faces will be detected.

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