Machine Learning Analytics Algorithms in Face Recognition

Every human is unique, and that’s why businesses are leveraging the unique aspects of Personal Data to make their connections more personalized. With the rise of personal data utility and cyber-crime detections, businesses are snapping Face Recognition start-ups with lucrative deals and acquisition offers. Out of 500 AI ML start-ups in the world, nearly 60 are working solely on personal data extracted from Face Recognition, Biometrics, Eye Detection and so on.

Here are top algorithms pushing the bar higher for Machine Learning and Analytics training in Bangalore.

Eigenfaces

If you have been seriously scouting for Face Recognition techniques, you would have definitely come across Eigenfaces. These eigenfaces are based on vectors and 3-D pattern identification. Been around for the last 3 decades, Eigenfaces form the core of many ML based Facial detection and digital photography algorithms.

Eigenfaces, together with Mathematics, Machine Learning and Principal Covariance Analysis (PCA) make up for 90% of the component that are included in any of the modern face recognition algorithms.

Eigenfaces are also used for lip syncing, video interfacing, recreating Pharoah and Mummified faces, voice recognition and simulating hand-gestures and mimicking sign language.

OpenCV Face Recognition

OpenCV face Recognition algorithm is a popular Python-based program. It uses various programming steps for face detection, face embedding and finally leveraging deep learning techniques. In its basic application, OpenCV uses face recognition pipeline to first detect the presence of face and location of the face in the image. It is still in its infancy to detect faces in real time. The future of OpenCV depends on the integration of Python, Sci-kit libraries with Computer Vision and Image Recognition technologies.

To help computers learn which face to detect, Machine Learning online training professionals use basic models such as k-NN Classifier and Random Forest. It can be used with dlib and face_recognition libraries.

Pattern-based Neural Networking

New technologies like the AppleID use convoluted neural networking for face recognition. Some companies like CloudFactory use Machine Learning technology to build not just face recognition platforms but also Sentiment Analysis and Image Moderation. All these Neural Networking platforms are based on the quality of ML models handling unsupervised data processing.

Trace Transform + Kernel

The Trace Transform Method is currently perceived as the next disruptor in the ML industry for Face Recognition companies. It can identify faces under transformation and trace out the future appearance of any principal object. This is based on the use of Artificial Neural Network technique that some AI ML research labs are currently patenting for Image Filtering, Pre-processing and Face Extraction processes.

Identifying humans using Machine Learning algorithms is coming to get you. Are you prepared for the challenge yet!

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