FACIAL MICRO EXPRESSION, AGE AND GENDER RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK

Registration ID: IJNRD_208296

Published ID: IJNRD2311086

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Laxmi H, Dr. Prabha R

Deep Learning, Image segmentation, Edge Detection, Neural Networks, face features, Convolutional Neural Network.

Identification of facial micro-expressions, emotions and these micro-expressions is a basic element of human and social interaction. Micro expressions are the result of voluntary and non-voluntary emotional reactions to stimuli that last for a few microseconds. Facial recognition and emotional identification is useful for biological medical applications such as surveillance, safety, brain monitoring of epilepsy and paralysis patients, and human computer interaction. The technology used in this paper is Convolutional Neural Networks (CNNs), which involve constructing characteristic maps using filtering data as input to do the convolution. The neural network is part of the artificial neural network, which is mainly applied to image detection and classification processes. The results obtained by testing a model composed of several input images give the output. The model recognizes the micro expressions of the face and highlights the output image. The accuracy of the group's image detection proves that the system saves time otherwise spent identifying each individual's emotions, but also have high accuracy for images of multiple people. Age assessment plays a leading role in applications such as biometric assessment, virtual makeup and virtual demonstration applications for jewelry and eyewear by mapping the face according to the found age. Lens Kart is an application that gives customers the option of trying it out. Age estimation is a subfield of facial recognition and facial tracking that in combination can predict individual health. Many medical applications use this mechanism to monitor their daily activities to keep track of their health. China uses this face detection technique for the identification of service drivers and the identification of Jaywalker. A number of essential machine learning algorithms to predict age and gender have been used. CNN methods are used to find an age and gender identification. In this implementation, Open CV and CNN has been used to predict the age and gender of a given person.

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Laxmi H & Dr. Prabha R (November-2023). FACIAL MICRO EXPRESSION, AGE AND GENDER RECOGNITION USING DEEP CONVOLUTIONAL NEURAL NETWORK. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(11), a789-a795. https://ijnrd.org/papers/IJNRD2311086.pdf

Volume 8 Issue 11, November-2023

Pages : a789-a795

Paper Reg. ID: IJNRD_208296

Published Paper Id: IJNRD2311086

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Bangalore, Karnataka, India

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