TY  - CONF
PB  - Institute of Electrical and Electronics Engineers Inc.
AV  - none
N2  - Age estimate using facial images is a fascinating and challenging issue. The characteristics from the face images are utilized to assess people's age, gender, ethnic origin, and emotion. Among this group of characteristics, age estimates can be beneficial in numerous possible real-time applications. Deep learning has recently achieved great success. Hence, we are using the Generative Adversarial Network (GAN) based method for automatic aging of faces. GAN produces images by altering facial attributes, and we create them to preserve the original person's identity in any age version. The deep generative networks have exhibited a remarkable capability in image generation. To the end, we introduced an approach for Identity-Preserving and GAN's Latent vector optimization. The evaluation of the objective of the proposed method demonstrates the following results proposed framework produced more realistic by comparing the state-of-art and ground truth. It can also be used for cross-age verification. We will be using the Dataset of MORPH and CACD to train our GAN model as it requires much data to learn. Moreover, an adversarial learning technique is presented to train a generator and parallel discriminators simultaneously, resulting in smooth continuous face aging sequences. © 2021 IEEE.
SN  - 9781665400916
KW  - Deep learning; Face recognition; Multiobjective optimization
KW  -  Age estimation; Age progression; Age recognition; And age estimation; CACD; Characteristic age; Face images; Face Verification; Facial images; Learning approach
KW  -  Generative adversarial networks
TI  - Deep Learning Approach For Facial Age Recognition
Y1  - 2021///
ID  - scholars15354
N1  - cited By 0; Conference of 4th International Conference on Innovative Computing, ICIC 2021 ; Conference Date: 9 November 2021 Through 10 November 2021; Conference Code:176892
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126724232&doi=10.1109%2fICIC53490.2021.9692943&partnerID=40&md5=693c793fc63f5db9abe6dc0d44027450
A1  - Muneer, A.
A1  - Ali, R.F.
A1  - Al-Sharai, A.A.
ER  -