Human Age and Gender Estimation using Facial Image Processing

Rahman, Sajjadur; Arefeen, Asiful; Mridul, Shashoto Sharif; Khan, Asir Intisar; Subrina, Samia

doi:10.1109/tensymp50017.2020.9230933

Cited by 9 publications

(2 citation statements)

References 21 publications

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“…The work of perceiving age and orientation with low-execution speed. Syed Taskeen Rahman, Asiful Arefeen, Shashoto Sharif Mridu [3], and others drew in with this assessment paper guided their audit to evaluate human age and direction measures by taking care of facial pictures. In this paper, two separate techniques were completed with adequate execution time and capability to evaluate both human age and direction from facial pictures.…”

Section: Related Workmentioning

confidence: 99%

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

M. R.

2023

IJRASET

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Humanity has made advancements in innovation pushing the boundaries of what was deemed impossible. With the help of cutting edge technology we can now determine a persons age and orientation by analyzing their image or video footage. This paperprovides an overview of the process including the techniques, strategies and algorithms involved. Unlike methods that treated these aspects separately this approach seamlessly integrates them all. It emphasizes the significance of this breakthrough. Explores its applications, in our daily lives. The main objective of this endeavor is to develop an age and orientation detector that utilizes Convolutional Neural Networks (CNN) and OpenCV to analyze features in images. Furthermore this paper showcases real world scenarios where this technology can be applied. Ranging from security services CCTV surveillance and law enforcement, to dating apps and matrimonial websites. Age and orientation estimations are determined based on image characteristics.

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Section: Related Workmentioning

confidence: 99%

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

M. R.

2023

IJRASET

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“…Since the last decade, deep CNNs, such as AlexNet [37], Inception [39], VGG [40], ResNet [41], and ResNeXt [42], ShuffleNet [43], MobileNet [44], RegNet [45], Transformers [46] and EfficientNet [47] have been successfully used for computer vision tasks. Their application for face recognition has been highly successful in accurately determining an individual's age, gender, and weight based on facial features extracted by deep CNNs from their faces [48][49][50][51][52][53][54][55][56]. In this study, we used tongue images rather than facial images to predict an individual's age, gender, and weight.…”

Section: Introductionmentioning

confidence: 99%

Using advanced convolutional neural network approaches to reveal patient age, gender, and weight based on tongue images

Wei

et al. 2023

Preprint

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The human tongue has been long believed to be a window to provide important insights into a patient's health in medicine. The present study introduced a novel approach to predict patient age, gender, and weight inferences based on tongue images using pre-trained deep CNNs. Our results demonstrated that the deep CNN models trained on dorsal tongue images produced excellent results for age prediction with a Pearson correlation coefficient of 0.71 and a mean absolute error of 8.5 years. We also obtained an excellent classification of gender, with a mean accuracy of 80% and an AUC of 88%. The model also obtained a moderate level of accuracy for weight prediction, with a Pearson correlation coefficient of 0.39 and a mean absolute error of 9.06 kilograms. These findings support our hypothesis that the human tongue contains crucial information about a patient. This study demonstrated the feasibility of using the pre-trained deep CNNs along with a large tongue image dataset to develop computational models to predict patient medical conditions for non-invasive, convenient, and inexpensive patient health monitoring and diagnosis.

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CNN based Music Recommendation system based on Age, Gender and Emotion

Jayakumar

Supriya

2022

2022 6th International Conference on Electronics, Communication and Aerospace Technology

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Human Age and Gender Estimation using Facial Image Processing

Cited by 9 publications

References 21 publications

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

A Multi-Modal Approach for Age and Gender Revelation for Enhanced Voting Systems

Using advanced convolutional neural network approaches to reveal patient age, gender, and weight based on tongue images

CNN based Music Recommendation system based on Age, Gender and Emotion

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