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Gender Classification and Age Estimation using Neural Networks: A Survey

Researchers have shown more interest in soft biometrics area to fill the commination gaps between humans and machines with the growth of real-world application has increased day to day life. Soft-biometric consists of age, gender, ethnicity, height, facial measurements and etc. This paper contains a detail discussion about the contribution of the researchers in the area of gender classification and age estimation using neural networking. Most of the work is done using Convolutional neural networks and auto encoders. Various elements related to neural network model such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research. Finally, the authors summarize germane tasks for future various research aspects.


INTRODUCTION
A Human face provides a lot of information about the age, gender, mood etc. It is affected by many dynamic factors that get changed over period of time such as aging, hair style, expressions, etc. Gender and Age are considered an important biometric attribute for human identification. Bio-metric recognition is the method of gathering information about person's physiological and behavioural characteristics for human identification and verification (security models). Biometrics consists of soft biometric (age, gender, ethnicity, height and facial measurements) and hard biometric (Physical, behavioural and biological). Soft-biometric attributes like skin, hair colour, distance between eye and nose, face shape, and etc can be accessed to accelerate data traversing, or to classify unlabelled subject for various gender and age classes.
Furthermore, with the widespread use of computers, biometric identification comes into demand in areas such as home automation and healthcare. Recently, it has come about automatically detecting physical presence and confirming one's identity through pattern recognition, computer vision and image analysis.
One of the Biometric attributes considered is aging. Aging is caused because of many reasons like DNA change, metabolism changes, UV rays from Sun, variation in facial tissues, Restructuring of facial bones etc. Face recognition system are adversely affected by the aging of the face. This idea plays huge role for new research area to be probed in the field computer vision. The age estimation is just carried out extensively to find out some patterns and variations as well as to get the best possible way to find out the various characteristic's that should be thought of. Another attribute is gender. Automatic gender classification is an important for many applications like surveillance, targeted advertisement's etc. This is done to differentiate between male and female based on the features of humans.
This literature elaborates descriptive minutiae and comparisons that are performed by author on various aspects like age, gender, and race. Also, different methods for extracting features, classification, evaluation for significant knowledge of research. This helps the enthusiastic researchers to enrol with deep learning aspects in classifying age and gender through human facial images.

FACIAL GENDER AND AGE ESTIMATION: APPLICATION AND CHALLENGE'S
Automation has covered wide area in real-world application area. Similarly, age and gender estimation can also be devised automatically. Age and gender classification techniques can improve computer's perceptional and interactional capabilities. Various applications for age and gender classification includes human-computer interaction (HCI) can provide users appropriate and customized services based on gender and age ground. Electronic customer relationship management is an internet-based technology; here personal data is examined for all class of consumers without intruding data privacy. Major aim of automation for classify age and gender is to predict age and gender class for each customer based on their facial images taken through camera and for demographic data. Age and gender classification can provide knowledge to improve the user experience in gaming and phone application. It can also be used for the verification or authentication accuracies based on soft biometric techniques. Age classification can control entry to unwanted content in televisions and Internet from children. In employment like police, military and government employments require age estimation during recruitment and while retirement age. In security and surveillance restricting of children access from adult vending machines like (alcohol and cigarette) and adulting websites movies, and monitoring of fraud detection. It can play major role in recognition of lost people, also for recognition of old people for purposes of identification from its previous images.
Facial gender classification and age estimation have many challenges. Gender prediction is followed for two-class that can be male or female. It is easy for human but not for machine to classify gender easily. For gender classification may methods and models have been bullied based on additional information from hairstyle, body shape, clothing and facial features. While in age estimation, it is not feasible to predict authentic age as of now. So, age grouping is still utilized for age prediction using facial images. Moreover, number of good datasets for age estimation and gender classification are limited for conduction of intense research.

CONVALUTIONAL NEURAL NETWORK
CNN is an active architecture of approach of profound learning which takes input as multimedia like images, videos or various 2D/3D data. It is evident that gender and age estimation on basis of facial features deals with images and video data. Thus, various researches done by the researcher's and scientist lies on facial gender and age estimation focuses on CNN models. CNN model consists of different weights for every hidden neuron expressed in mathematical expression of multi-dimensional matrix. Further image transfers through hidden layers of CNN, dimensions of weights and matrix gets remodel after every convolutional layer. CNN frameworks are designed by a well-fixed combination of layers such as narrow layers, sub sample layers, and full layers. Concentrated layers serve as building block to CNN and it is used to perform the basic function of convolution. Sub-sample layers perform major function for controlling over fitting issues by reducing the parameters and size by using max operation for maximum pooling. At last the fully connected layer of neurons is maps to all activation function of previous layers. There exists an additional RELU layer that helps in implementing the nonpurity function and correction in CNNs. The last layer is usually utilized as softmax layer that provides decimal feasibility to each output neuron. Researchers have proposed several interactive CNN architectures for basic neural network concept.
Predefined CNN architectures were investigated for this research keeping into account the recognition of gender as well as the estimation of the age from facial images, such as: VGG19 and VGGFace, ResNet, AlexNet and GoogLeNet.

VGG19 AND VGGFace
VGG is a deep CNN architecture designed by Karen Simonyan and Andrew Zisserman at Oxford. VGG19 consist of 19 weight layers in the network and so named as VGG19.
In 2014 the VGG16 famed in the ImageNet competition as most accurate architecture and was correlate with InceptionNet. However, in some instances the additional hidden layer of VGG19 was advantaged to get more accurate findings than VGG16. Basic VGG architecture consists of layers that each have maximum pooling layers. Two fully associated layers are comprising under every VGG designs having size of 4096 with the dropout of 50%, along with a size 1000 layer of softmax.

ResNet
ResNet is an abbreviation for Residual Network. ResNet introduced shortcut connections within layers to enable crosslayer connectivity, which are data independent and parameter free compared to the gates of the highway network. In highway networks, the non-residual functions are observed from layers when a gated shortcut is shutdown.

AlexNet
In 2012 Alex Keyjevski design a neural network and therefore known as AlexNet. It is a combination of 8 layers in total. 5 of them are layered layers. Max-pooling layers are followed by some of these concentric layers. The final three layers are completely interconnected and known as fully connected layers. A ReLU layer is connected behind fully connected layers. Overfitting issues is controlled through dropout layer which is usually connected after the fully connected layer. AlexNet noted 16.4% for top-5 error.

GoogleNet
GoogLeNet is designed by inter-connecting nine Inception modules to recommend a deeper architecture. As a part of basic CNN architecture, Maximum pooling layers that helps in reducing the parameter size follows some inception modules for GoogLeNet architecture. Unlike Alex-Net and VGG-Net architecture, GoogLeNet does not consider a firm Convolution size. Generally, sizes of convolutional kernels are 1 × 1, 3 × 3 and 5 × 5. The 1 × 1 convolution layers implements feature dimensionality reduction function that is processed to broad network. GoogLeNet exceed AlexNet and VGGNet as conclusion to top-5 error that reduces to 6.67% by using 22 convolutional layers.

FACIAL GENDER CLASSIFICA-TION AND AGE ESTIMATION BASED ON NEURAL NETWORK
Brief summary of research covering each individual neural net based facial characteristics, gender and age estimation, examined by the researchers between 2015 to 2019 is given bellow.

CNN [Gil Levi et al.]
Author has recommended CNN architecture for classifying gender and age using benchmark database of adience facial images with a better performance. The architecture is built up of one output layer and three fully connected layers. A part of starting two layers of convolutional layers contains local response normalization layer and connected to repaired linear operation (ReLU) along with the max-pooling layer for each convolutional layer. Smaller network design has been introduced to reduce over fitting problem. Every entirely associated layer encloses 512 neurons all. Output of the neural network is depending upon SoftMax layer for which input is provided through output of previous entirely associated layer. Cropped and over sampled facial mugshots where used for gender and age estimation. Author noted Mean accuracy ± Standard error for age as well as the gender, where classification of the age using misperception matrix. Literal efficiency of single cropped image for estimating age results to 49.5 ± 4.4 and for 1-OFF the efficiency results to 84.6 ± 1.7. Whereas, over-sample method results efficiency of 50.7 ± 5.1 and 1-OFF efficiency of 84.7 ± 2.2.

Ranking CNN
[Shixing Chen et al.] proposes a framework that generates binary output for sub networks that at last combines to obtain the age labels for age classification by giving input facial images. Independently, features were learned during designing all array of age class. Due to which various age class patterns were discovered and leads to estimated evaluation. To reduce over fitting labelled data was used and each age group was trained individually. For ranking of ages, authors have provided a tighter error constraint that is average analysis of all errors evaluated by classifiers. Three theorems were proven in this research paper.

LMTCNN (Lightweight Multi-Task)
[Jia-Hong Lee et al.] They presented a powerful CNN known as lightweight multi-task CNN for age assessment along with the gender acknowledgement. To trim model size as well as including the time of inference in depth-wise divisible convolution is utilized by Lightweight multi-task convolutional neural network which were critical for mobile applications. For feature extraction only one CNN is used and as well as for various function in their system. The point wise convolution is a convolution has 1 × 1 kernel's size, as well as it associates the standards of output in depth-wise convolution. It composed of one common layer of convolution, two depth-wise separable layers of convolution along with 2 fully associated layers. Network was implemented using the Tensorflow framework on their machine by means of Intel Xeon E5 3.5 GHz CPU, 64G RAM and GeForce GTX TITAN X GPU.

Hybrid architecture (CNN -ELM (Extreme Learning Machine))
[Mingxing Duan et al.] Features extraction is performed utilizing ELM and CNN is actually used for the goal of outcome distinction. The writers offer information of level and parameter choice developing, actions followed to stop over fitting, as well as detailed derivation of the backpropagation activity. Adience as well as the MORPH data source is utilized to get outcomes that are predictable. Experimental outcomes have proved its validity of particular hierarchical hybrid type for lots of real time uses. Just earlier detailing the suggested perform, the experts explain ELM as well as CNN in information for readers' lucidity. Crossbreed CNN ELM structure features a fair plan for developing constant level, difference normalization level, maximum pooling level as well as ELM distinction layer. The concentric level does convocation with the prior levels & consequently extracts capabilities of interest. Distinctions normalization enables the calculation of various characteristic maps in the exact identical spatial place through utilizing level removal as well as segmentation activities. Max-pooling levels toss everything irrelevant information and just include information that is crucial. Lastly, the outputs of the total interconnection level are actually attached as inputs to the ELM as well as distinction effort are achieved. CNN ELM processing is completed as a program stage as well as a distinction stage.
Error rates age as well as the reliability along with the gender abortions is actually the parameters utilized to confirm the improved functionality of given particular structure as of state-of-the-art criteria. For the era distinction, the reliability is actually discovered to be 52.3 % and MAE 3.44.

OTHER METHODS FOR FACIAL GENDER CLASSIFICATION AND AGE ESTIMATION
This section contains a brief summary of other techniques like Classification, regression and hybrid methods used for gender recognition and age classification. Other than above all models of neural nets for gender recognition and age classification. Survey has provided a superior option's for gender recognition and age classification for all methods which will lead to better and wide understanding.
[Michal Uricar et al.] proposed controlled production of SVM for prophesying gender and age from a facial image using deep features exploited from DEX. Since for gender they used a binary SVM classifier and they trained 10 dissimilar splitting of the dataset of ChaLearn LAP 2016 in various multi-class SO-SVM classifier varieties for the estimation of probable age. Each multiclass SO-SVM predictor need a value of SoftMax normal refinement. Author terminates the finest is to associate the illustration capability in terms of the deep structures through the robustness capability of SO-SVM model for prophecy.
[F. Dornaika et al.] For training and testing phase regression tool were used in both phases. Thus, they also sympathetic in efficient shallow regressors. They used PLS (Partial Least Square) regressor. It's a statistical method that can discover relationship between groups of observed variables X and Y whereas X shows the observation and Y associated response. PLS is a powerful statistical approach which can achieve dimensionality reduction and classification/regression similar to PCA. Which will reduce the dimensionality of dataset?

DETAILED DISCUSSIONS
Literature undergoes thorough inquiry of facial gender and age classification using neural nets have been done. All the work comes under this domain has been noted and discussed. For all the interested researchers it provides a summary of human facial gender classification and age estimation using neural network. Various research works placed under neural nets are reviewed from various condition and technicalities like different type of models, dataset used, main inputs, evaluation metric and results are presented in tabular form.
Other methods for gender classification and age estimation have been discussed in the section above for better understanding.  It's well worth noting that the most popular datasets for gender as well as years distinction analysis are actually Adience datasets and MORPH. MORPH can be obtained to public within Creative Commons (CC) license along with it consists of 2 albums. Album one includes of 515 digital pictures as well as 55,134 digital pictures of 13,000 individuals is contained by album two. These pictures are actually taken during various spans of occasions to get age variants as well as album two is currently growing. The majority of the analysis functions in Table 1

Fig 1: Datasets used in various references
Explanation of the ratio of using of various older directories in various research documents working with a pie chart is actually displayed above in Fig.1 Optimum place is actually taken in place by the MORPH data source as well as bare minimum is actually taken up by PAL repository and several recently produced directories in various research documents as BEFA and UTKFace (Abhijit Das et al. 2019) along with Private Datasets of Asian. Collective Score may be clarified by the formulation provided as: Right here N once again describes the selection of images underneath test as well as N e≤k matter directs the number of examination pictures whose complete mistake involving the estimated and actual age is actually under k years.

CONCLUSION AND FUTURE WORK
Overall study of contribution made on gender classification and age estimation can be used in to solve the real-time application problems. In this paper most of the research work done is in Convolutional Neural Networks. Eleven unlike types of neural networks have been discussed with their MAE and accuracy obtained by models. Additionally, function extraction in addition to distinction of a few functions is actually carried out using just an individual element extractor or maybe a one-time classifier along with in various additional works, fusion is actually followed to do distinction process or maybe attribute extraction.
On the future direction, results that are Good for gender recognition as well as years opinion can continue to be received utilizing transfer learning strategies with expansion in reliability. Combos of fusions as well as datasets of attributes might be what is on the horizon for the development of rich learning and from 2D to 3D Facial Data. Moreover, ethnicity estimation, Affective behaviour analysis and numerous additional demographic features could be verified for the performance of them by the classifier of Neural Networks.

ACKNOWLEDGEMENTS
I would like to thank my guide Dr. Nitin N. Pise for his support in this survey. Also, Siddhivinayak Kulkarni Sir for helping me the best way possible.