Gender recognition using motion data from multiple smart devices

Dong, Jianmin; Du, Youtian; Cai, Zhongmin

doi:10.1016/j.eswa.2020.113195

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…They have been trained various classifiers on many images from the audience benchmark with multiple train and test data splits. In [21] authors use motion data from multiple smart devices in their gender recognition approach. The collected motion data are according to three aspects: time, frequency, and wavelet domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition

Ounachad¹

2020

IJETER

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Machine learning is a subarea of artificial intelligence based on the idea that systems can learn from data and make decisions automatically. Bayes Theorem is widely used in machine learning. The main objective of this paper is to classify the gender of the human being based on their face sketch images by using a golden ratio features and Bayes Classifier. This paper presents a method for human face sketch gender classification and recognition. It is inspired in our other model which was pre-trained on the same task, but with sixteen features and fuzzy approach. Toward this end, just two features will be extract from the input face sketch image based on two face golden ratios. The detection stage passes by Viola and Jones algorithm. The classification task is evaluated through Bayes classifier. An experimental evaluation demonstrates the satisfactory performance of our approach on CUFS database with 80% for training, 20% for testing. The proposed machine learning algorithm will be a competitor of the proposed relative the stat of the art approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition

Ounachad¹

2020

IJETER

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“…Therefore, we need the help of a computerised system to observe gender recognition with a particular purpose. Some of the uses of gender classification applications [5] include security system monitoring [6] [7], marketing strategies in shopping centre [8] [9], surveillance for advertising targets [10], and human-computer interaction applications [11] [12]. The fields of science that process biometric data of face [13][14] [15] are usually image processing and computer vision.…”

Section: Introductionmentioning

confidence: 99%

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Adhinata¹,

Junaidi²

2022

IJCDS

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Gender classification using human face data becomes a trending topic for researchers in the field of image processing and computer vision. The human face is biometric information that can be used to differentiate gender using a computer-aided system. Previous research utilised a local feature algorithm for extracting features on the face. However, the processing speed for one image was more than 2 seconds, making it unsuitable for real-time processing using video data. Processing video data requires a fast feature extraction algorithm because video data collects sequential images (frames). Moreover, the gender classification system's success is also measured by its accuracy, consequently it is necessary to choose the correct classification method to divide the two classes of men and women. In this research, we propose the FaceNet algorithm for feature extraction and explore several supervised machine learning methods (KNN, SVM, and Decision tree) appropriate for gender classification on video data. This study used 23,000 training data on each gender. From the experiment, combination of the FaceNet algorithm and KNN method resulted in the best accuracy of 95.75% with a processing speed of 0.059 seconds on each frame.

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“…Sports social networking is a new social model based on sports. Due to the rise of online social networking and the continuous encouragement and support of the state for sports, sports social networking is for young people, it has become a new way to communicate with friends [12][13][14]. Sports social networking meets the social needs of modern young people and brings stable user traffic and large user stickiness to sports APP.…”

Section: Introductionmentioning

confidence: 99%

Wearable Running Training Data Acquisition System Based on Intelligent Computer Technology

Liu

Chang

Cao

et al. 2022

Computational and Mathematical Methods in Medicine

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Because the instantaneous motion of running training changes rapidly, the acquisition accuracy is low. Therefore, a wearable running training data acquisition system based on intelligent computer technology is designed. In the hardware design of the system, by setting the registers of the main control chip, inertial sensor, and magnetic sensor, the acceleration and angular velocity information of motion exist in the data format of binary complement. Bluetooth realizes the transparent transmission of data between hardware and software. The system software uses LDA recognition algorithm to decompose the dynamic data transmitted by Bluetooth into multiple static data and collect the final motion data through training. The experimental results show that the designed system has good quality and high accuracy in motion detection and counting.

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Gender recognition using motion data from multiple smart devices

Cited by 13 publications

References 17 publications

Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition

Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition

Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Wearable Running Training Data Acquisition System Based on Intelligent Computer Technology

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