Technical Details

Face mapping technologies would measure the space between various points on a human face, such as the distance between the eyes, the curve of the cheekbones, and the depth of the eye sockets, and then identify other faces that matched the original face distinctive biometric traits. Today, a network is trained by being exposed to tagged data, which powers facial recognition using Deep Learning techniques. A network gets trained, or made capable of detecting, identifying, and classifying data, when it is exposed to more data over time, mimicking how a human is taught and learns.

To predict the age and gender of a subject, researchers have created a variety of algorithms combining categorization and ML concepts. The most fundamental kinds of algorithms can be used to create several secondary algorithms with enhancements. The Principal Component Analysis (PCA)-based Eigenfaces approach finds a linear combination of traits that minimizes the total variance in the data. If we want to determine the gender of the subject, we need to learn the distinguishing features of both classes. The Eigenfaces technique, an unsupervised statistical model used for gender determination, is based on principal component analysis. He achieves a 98% identification rate with subject-independent cross-validation, which is not very suitable for Fisherface method.

Our service uses a model trained on a dataset consisting of images with faces labeled with age, gender, and attractiveness. Optimization algorithms ensure that models learn to generalize. That is, it also learns to predict the attractiveness of images that are not part of the dataset, with the help of machine learning techniques such as linear regression etc...