FaceAge Modeling: A Pattern Recognition Analysis for Age Estimation This One way to explain the concept of face recognition is to see it as the computational ability engaged in verifying the identity of a person on the basis of the face image regardless of background, faceattachment, facemakeup and illumination. Face recognition has been researched and applied in real life problems such as security access controls, banking identity verification, immigrant verification and licensing. This study examines popular techniques and approaches involved in face recognition and its application in age estimation. The identification characteristics possessed by the face with other demographic information such as age, sex and ethnicity can be predicted using facial features extracted from facial images. The application of face recognition in these areas especially age estimation has not been richly researched. Consequently, this study presents an explorative writeup of faceage models with reference to the application of the commonly used face recognition technique to computationally achieve age prediction. In this study, itu0027s germane to consider face recognition techniques since an important part of age estimation pipeline is the feature extraction which is also important in face recognition. This study shows that most of the researches in age estimation were tested with image taken under controlled or semicontrolled environment which are not sufficient to capture challenges in real life conditions. It was observed that most existing researches uses FGNET and MORPH 2 face database in testing age estimation systems with scarcity of black face feature across several existing systems. However, more work is desirable, owing to failure of some system to accurately recognize or identify specific faces or using long turnaround processing time for identification visavise age estimation. This calls for the development of robust blackface database for local testing and implementation among African population. It is also evident that more work is required to show the interaction of epigenetics and its analysis in faceage modeling.

Face Image Age Estimation Based on Data Augmentation and Lightweight Convolutional Neural Network Face images contain many important biological characteristics. The research directions of face images mainly include face age estimation, gender judgment, and facial expression recognition. Taking face age estimation as an example, the estimation of face age images through algorithms can be widely used in the fields of biometrics, intelligent monitoring, humancomputer interaction, and personalized services. With the rapid development of computer technology, the processing speed of electronic devices has greatly increased, and the storage capacity has been greatly increased, allowing deep learning to dominate the field of artificial intelligence. Traditional age estimation methods first design features manually, then extract features, and perform age estimation. Convolutional neural networks (CNN) in deep learning have incomparable advantages in processing image features. Practice has proven that the accuracy of using convolutional neural networks to estimate the age of face images is far superior to traditional methods. However, as neural networks are designed to be deeper, and networks are becoming larger and more complex, this makes it difficult to deploy models on mobile terminals. Based on a lightweight convolutional neural network, an improved ShuffleNetV2 network based on the mixed attention mechanism (MASFV2: Mixed AttentionShuffleNetV2) is proposed in this paper by transforming the output layer, merging classification and regression age estimation methods, and highlighting important features by preprocessing images and data augmentation methods. The influence of noise vectors such as the environmental information unrelated to faces in the image is reduced, so that the final age estimation accuracy can be comparable to the stateoftheart.