Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock

Zhu, Zhiliang; Cheng, Yao

doi:10.1016/j.comcom.2020.02.003

Cited by 54 publications

(24 citation statements)

References 2 publications

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“…Zhu and Cheng [25] present. An efficient Attitude Tracking Algorithm (EATA) based on OpenCV is used for facial recognition in the Intelligent door lock system.…”

Section: Face Recognitionmentioning

confidence: 93%

Face Detection and Recognition Using Opencv

Suganthi¹,

Poongothai²,

Prashanthini³

et al. 2020

GOR

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Computer Vision is one of the most fascinating and challenging tasks in the field of Artificial Intelligence. Computer Vision serves as a link between computer software and the visuals we see around us. It enables computer software to comprehend and learn about the visuals in its environment. As an example: The fruit is determined by its color, shape, and size. This job may seem simple for the human brain, but in the Computer Vision pipeline, we first collect data, then conduct data processing operations, and then train and educate the model to learn how to differentiate between fruits based on size, shape, and color. The main goal is to identify and comprehend the images and offer new images that are more useful for us in different life fieldsThe term "OpenCV" is an abbreviation for "open source computer vision." The architecture is made up of software, databases, and plugins that are pre-programmed with support for integrating computer vision applications [3]. It is one of the most used toolkits with a large developer group. It is well-known for the size at which it builds realworld usage cases for industrial use. OpenCV follows C/C++, Python, Java programming languages and can be used to build computer vision software for desktop and smartphone platforms such as Windows, Linux, macOS, Android, and iOS. The most recent releases are OpenCV-4.5.2 and OpenCV-3.4.14. It is free and open-source, as well as simple to use and install. It is intended for numerical productivity with a heavy emphasis on real-time applications. The first version was in the C programming language; however, its success increased with the release of Version 2.0, which had a C++ implementation [2]. C++ is used to create new features. OpenCV can be downloaded for free from http://opencv.org. This platform includes the most recent distribution update (version: 4.5.2) as well as older iterations. Photos must be in BGR or Grayscale format in order to be displayed or saved via OpenCV. Otherwise, unfavorable outcomes could occur [1].Face detection is a form of computer vision that aids in detecting and visualizing facial features in captured pictures or real-time videos. This type of object detection technique detects instances of semantic artifacts of a given class (such as people, cars, and houses) in digital pictures and videos. Face recognition has become increasingly important as technology has advanced, especially in fields such as photography, defense, and marketing [4], [5].

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“…Zhu and Cheng [25] present. An efficient Attitude Tracking Algorithm (EATA) based on OpenCV is used for facial recognition in the Intelligent door lock system.…”

Section: Face Recognitionmentioning

confidence: 93%

Face Detection and Recognition Using Opencv

Suganthi¹,

Poongothai²,

Prashanthini³

et al. 2020

GOR

View full text Add to dashboard Cite

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“…Analysis of personality traits was carried out after the face shape was detected from the profile photo using python programming language and the OpenCV library [54]. A detailed explanation for each type of face shape can be seen in Table 1.…”

Section: F Evaluation and Personality Traits Analysismentioning

confidence: 99%

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

Purwono

Ma’arif

Wulandari

2021

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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The progress of a company is influenced by the excellent performance of its employee. The recruitment process should be done in a correct procedure so that it would not have the potential to harm the company. The improved use of social media can be an aspect to be applied in a recruitment process. LinkedIn is a social media platform that has many users which focuses on the career development aspect. Profile photos are commonly used in social media. In physiognomy, a personality analysis can be carried out based on his/her outward appearance. The profile photo can be an aspect of personality analysis with this knowledge. This research aimed to predict the face shape based on LinkedIn profile photos. A Cascade classifier algorithm with a haar-like feature was used to detect the face area. Dlib library was used to detect face landmarks. K-Means algorithm was used to differentiate the border of hair and facial skin. Indicators of the face shape calculation are the value of face angle, which is the arctangent of the face landmarks matrix; similarity value from the standard deviation calculation between horizontal line 1, 2, and 3; and diameter value which resulted from the standard deviation calculation between horizontal line 2 and vertical line 4. We provide output as face shape from the LinkedIn profile photos. Based on ten profile photo samples, only two predictions were incorrect.

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“…Fig. 2 The general H/W configuration 2) S/W Operation: To implement proposed system, OpenCV [32] library and Python programming language [33] were used. The system takes images of braille blocks in realtime with the Pi Camera and helps the user walk safely using voice guidance when braille blocks are unavailable.…”

Section: ) H/w Configurationmentioning

confidence: 99%

An Application on Visual Cognitive Assistance system for Braille Block Recognition Based on Raspberry Pi System

Kim¹,

Lee²,

Son³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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There are many visually impaired people living their lives throughout the world. As of now, the only realistic tools available for these people are white sticks and guide dogs. However, these visual aids are insufficient for handling unexpected obstacles in various environments. Accidents tend to occur frequently, and these aids are very costly, thus limiting their usefulness for the visually impaired. This paper aims to develop a visual aid cognitive system and explores the possibility of practical performance. The overall system configuration uses the Raspberry Pi and OpenCV in real-time to capture braille block images and recognize the braille blocks' pattern. It also gathers information about the surrounding environment before conveying the information to the user. In the first phase, the Go/Stop signal is determined by the linear or circular braille block according to the type of braille block. In the second phase, the visual aid system equipped with an ultrasonic sensor warns the user when encountering an obstacle so that the user can walk safely. We implemented a text recognition system based on a Text-to-Speech converter that transmits the text recognition information to the visually impaired and tested TTS function to read various street signs with various fonts. We lastly discuss the future possibility for our auxiliary system in various road environments and how it can be improved to help visually impaired people.

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Application of attitude tracking algorithm for face recognition based on OpenCV in the intelligent door lock

Cited by 54 publications

References 2 publications

Face Detection and Recognition Using Opencv

Face Detection and Recognition Using Opencv

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering

An Application on Visual Cognitive Assistance system for Braille Block Recognition Based on Raspberry Pi System

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