Age and Gender Predictions using Artificial Intelligence Algorithm

Ghildiyal, Anirudh; Sharma, Sachin; Verma, Ishita; Marhatta, Urvi

doi:10.1109/iciss49785.2020.9316053

Cited by 23 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The recognition of sex in the field of technology has been actively developed through computer vision focused on the body [5] and face [6] as regions of interest. Furthermore, data related to keystroke dynamics [7], ear shape [8,9], tweeting [10], voice [11], and gait [12] have provided enough discrimination space for a sex recognition classifier.…”

Section: Introductionmentioning

confidence: 99%

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

Lopez

Parra

Buitrago

2023

Data

View full text Add to dashboard Cite

Human sex recognition with electrocardiogram signals is an emerging area in machine learning, mostly oriented toward neural network approaches. It might be the beginning of a field of heart behavior analysis focused on sex. However, a person’s heartbeat changes during daily activities, which could compromise the classification. In this paper, with the intention of capturing heartbeat dynamics, we divided the heart rate into different intervals, creating a specialized identification model for each interval. The sexual differentiation for each model was performed with a deep convolutional neural network from images that represented the RGB wavelet transformation of ECG pseudo-orthogonal X, Y, and Z signals, using sufficient samples to train the network. Our database included 202 people, with a female-to-male population ratio of 49.5–50.5% and an observation period of 24 h per person. As our main goal, we looked for periods of time during which the classification rate of sex recognition was higher and the process was faster; in fact, we identified intervals in which only one heartbeat was required. We found that for each heart rate interval, the best accuracy score varied depending on the number of heartbeats collected. Furthermore, our findings indicated that as the heart rate increased, fewer heartbeats were needed for analysis. On average, our proposed model reached an accuracy of 94.82% ± 1.96%. The findings of this investigation provide a heartbeat acquisition procedure for ECG sex recognition systems. In addition, our results encourage future research to include sex as a soft biometric characteristic in person identification scenarios and for cardiology studies, in which the detection of specific male or female anomalies could help autonomous learning machines move toward specialized health applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

Lopez

Parra

Buitrago

2023

Data

View full text Add to dashboard Cite

show abstract

“…Abirami et al (15) employed CNN to identify the age and sex from same face, since the approach showed less accuracy and precision and didn't meet the requirement of gender identification in real time applications. The authors of (16)(17)(18) suggested an Artificial intelligence based methods to analyze age group and gender of a human the methods have more delay and reduces overall throughput. Swaminathan et al (19) proposed a technique in which numerous Machine Learning Classification Procedures on Facial image has been considered to identify the gender.…”

Section: Introductionmentioning

confidence: 99%

Visual Mapping for Gender Identification from Facial Images using BAM and DON

Salvadi¹,

Rao²,

Vathsal³

2023

IJST

View full text Add to dashboard Cite

Objective: To Develop an intelligent and innovative method to categorize the Gender by focusing facial images. Method: We integrate the characteristics of Bidirectional Associative Memory (BAM) and Deep Octonion Networks (DON) to enhance the Gender detection in real time applications. The developed hybrid model is called Visual Mapping of BAM and DON (VMBAD). To validate the projected system, we make use of 4000 images and 126 different subjects as a data set to train the proposed approach and simultaneously compare our results with the existing methods using the same data set. Findings: The projected technique improves the performance of the system by 3 -5 % in terms of sensitivity, accuracy, and precision when compared with the existing approaches (vide figures 4-7). Novelty: The designed method enhances both the accuracy and precision of image by nearly 4% and 2% respectively when compared with the reported work.

show abstract

“…Sex recognition has been studied using body [5] and face [6] images, gait [7], voice [8], twittering [9], and keystroke dynamics [10], among others.…”

Section: Introductionmentioning

confidence: 99%

Sex Recognition through ECG Signals aiming toward Smartphone Authentication

et al. 2022

View full text Add to dashboard Cite

Physiological signals are strongly related to a person’s state of health and carry information about the human body. For example, by ECG, it is possible to obtain information about cardiac disease, emotions, personal identification, and the sex of a person, among others. This paper proposes the study of the heartbeat from a soft-biometric perspective to be applied to smartphone unlocking services. We employ the user heartbeat to classify the individual by sex (male, female) with the use of Deep Learning, reaching an accuracy of 94.4% ± 2.0%. This result was obtained with the RGB representation of the union of the time-frequency transformation from the pseudo-orthogonal X, Y, and Z bipolar signals. Evaluating each bipolar contribution, we found that the XYZ combination provides the best category distinction using GoogLeNet. The 24-h Holter database of the study contains 202 subjects with a female size of 49.5%. We propose an architecture for managing this signal that allows the use of a few samples to train the network. Due to the hidden nature of ECG, it does not present vulnerabilities like public trait exposition, light/noise sensibility, or learnability compared to fingerprint, facial, voice, or password verification methods. ECG may complement those gaps en route to a cooperative authentication ecosystem.

show abstract

Age and Gender Predictions using Artificial Intelligence Algorithm

Cited by 23 publications

References 5 publications

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

A Fast Deep Learning ECG Sex Identifier Based on Wavelet RGB Image Classification

Visual Mapping for Gender Identification from Facial Images using BAM and DON

Sex Recognition through ECG Signals aiming toward Smartphone Authentication

Contact Info

Product

Resources

About