The advancements in computer vision have made the use of data science for gender and age detection and prediction a lot easier to use and more accessible to people in general. For instance, in computer vision surveillance, age/gender prediction is often used. Because of its value in intelligent applications, studies have seen significant advances.
However, factors that affect the face, like gender and age, are essential, and determining them is the first step. Numerous companies use these techniques to help interact with customers, meet their requirements, and deliver an excellent experience. The gender of the person and their age help to identify and anticipate their requirements.
Even for us human beings, determining the gender/age from an image is difficult since it’s only based on the appearance of people that can change at times. Age-related people are likely to have more different looks than we imagine.
How To Use Data Science For Age Prediction
Age Prediction should be treated as a regression problem since we expect to get a true number as an output. However, accurately estimating the age with regression isn’t easy, and humans cannot accurately estimate age by simply the appearance of a person’s face. But, we do know whether people are still in their 20s and 30s.
This is why it is sensible to consider this issue as a classification problem in which we try to figure out the class of age the person is in. For instance, age in the range of 0-2 would be an individual class, the range of 4-6 is a different one, and the list goes on. The Audience dataset contains eight classes, which are divided into the main age groups to consider.
- 0 – 2
- 4 – 6
- 8 – 12
- 15 – 20
- 25 – 32
- 38 – 43
- 48 – 53
- 60 – 100
Therefore, the age prediction network includes eight nodes in the last softmax layer, indicating the age ranges. Keep in mind that age prediction based on a single photo isn’t such a difficult problem as you may think. Perse, because the perception of age is based on several key factors.
Understanding all this can be a breeze for you once you complete post-graduation in data science, and the course helps you understand the concepts and techniques in more detail. So, what is the outcome of the process like?
(1) Image Processing
Intelligent gender and age classifiers take on the task of classifying under non-filtered conditions in the real world. Many faces aren’t aligned or non-frontal and come with different degrees of variation in appearance, pose lighting, and background. So, the wild face images must first be identified, aligned then used as input to the classification algorithms.
(2) Face Detection
The initial stage of image processing includes face detection. This process determines the face’s location within an image. In this project, we use an open-source face detector called HeadHunter. In the process of detecting faces, all input images are rotated within an angle of between 90deg – 90deg angles with a move of 5 degrees.
Then, the detector picks the image that can provide us with the most impressive face detection result. However, if the face isn’t detected within all modifications to images, the initial image is upscaling, and the faces detection is repeated until a face has been identified. Upscaling will help in detecting faces in all images that you input. This is one of the great technology information we got in 21th century.
(3) Landmark Detection & Face Alignment
The next step to the Detection of faces is the Detection of facial landmarks and the face alignment phase. In this section, you have to apply the latest technology.
The image processing solution is an open-source face landmark recognition algorithm that depends on five landmark detection models, such as two half-profile models, frontal models, and two full-profile models.
The five models have been trained to operate with the appropriate facial postures. The face alignment process involves running five models of facial landmarks on the identified faces.
An affine transformation is carried out on the model with the maximum confidence score in accordance with the optimal preset settings of these landmarks.
(4) CNN Architecture
The CNN structure is a unique six-layer system, which comprises four convolutional layers and two completely connected layers. Its CNN design is a complete sequential deep learning structure that involves feature extraction and further classification phases.
The feature extraction phase includes four convolutional layers and the appropriate parameters, such as filter number, kernel size for each one, and the stride. The classification stage comprises two layers fully connected, which manage the classification stage in the algorithm. The first layer that is fully connected has 512 neurons, and a ReLU follows it.
The next step is batch normalization and then an exit layer with the dropout rate of 0.5. The second and final fully connected layer has more than 512 features assigned to either 8 or 2 neurons to perform task-based classification.
Below are some more keynotes:
Age Group Classification
You can test your classification method of a person into the appropriate age category. You must train the network to categorize faces into eight age-related classes and evaluate the accuracy of the classifier using the OIU-Audience data; a popular dataset used to test the current methods of age and gender-specific classification.
You also examine the method of identifying an individual as the correct gender. You can test the method’s performance using the identical Adience dataset containing gender labels. Furthermore, you can train your network to recognize and classify two classes, male and female.
After the testing session, you confirm the precision of these models. However, it is possible to improve the accuracy with more data, the augmentation of data, and more efficient networks. You can also use an equation model to replace classification to predict age when enough data is available.
This article has covered process, outcome, exact age group classification, and gender classification. Applying data science can improve numerous image processes, face detection, landmark detection, and CNN architecture. You can take a data science course in India to better grasp CNN and other related concepts.
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