Face detection – also called facial detection, plays a key role as the very first step in many applications. It is a computer technology used for face tracking, face analysis and facial recognition. Face detection when integrated with biometric security systems makes it possible to monitor and track people in real-time.
However, high-performance face detection still remains a challenge due to the dynamic nature of faces. Aspects like orientation or angle, light levels, clothing, accessories, hair color, facial hair, makeup, age, etc make it difficult for computers to detect faces.
In recent times, deep learning methods have achieved unparalleled results on standard norms for face detection. The growing demand of the technology makes it imperative for techies to master deep learning.
In this blog, we will acquaint you with methods that make it easier for you to master Deep learning for face detection. We will also discuss the scope and future of deep learning in the coming times.
What is YOLO?
You Look Only Once – YOLO is an algorithm that detects and recognizes various objects in a picture. The YOLO algorithm uses convolutional neural networks (CNN) to uncover objects in real-time. It can predict multiple objects in a single algorithm run through.
The object detection phenomenon in YOLO seeks to answer two questions-
- What is the object?
- Where is the object?
Why is the YOLO Algorithm important?
YOLO is a popular algorithm for face detection due to its speed and accuracy. It is faster than other algorithms due to its simple architecture.
- Speed: Since the algorithm is used to detect objects in real-time, the speed of detection improves using YOLO
- Accuracy: it is a predictive technique that does minimal background errors resulting in high accuracy
- Learning Capabilities: The algorithm can learn the representations of objects and apply them to detect objects in real-time
Face Detection With Deep Learning
The success of deep learning and CNN-based approaches has surpassed multiple face feature detection methods. In comparison to the traditional computer vision approaches, deep learning methods avoid the hand-crafted design pipeline.
Deep learning uses a trained bunch of neural networks for face recognition. The neural networks extract a bunch of features – also called face encodings or numbers, to describe a face. It compares the face encodings with different images provided and lets the user know if it matches with any of the images.
The neural network gets trained automatically to identify different features of faces and calculate numbers based on that. In a nutshell, the whole ideology of face recognition can be described as a process that first involves four steps:
1.Detect
The first step involves identifying one or more faces in an image and marking it using a bounding box. Since detection lays a base for recognition, faces must be robustly detected at this stage. Faces should also be aligned regardless of orientations, angles, light levels, hairstyles, hats, glasses, facial hair, makeup, ages, and so on. The face detection can further be divided into two categories:
- Feature-based: The feature-based face detection uses invariant features of faces for detection. Although fast and effective, this method can invariably fail sometimes, rendering the algorithms useless.
- Image-based: Image-based face detection is the most promising one as it relies on machine learning to automatically locate and extract faces from the entire image. This is a more holistic way of face detection and this is where neural networks come in.
Analysis
Next, the detected face in the image is captured and analyzed. The neural networks read the geometry and photometric of the face. This step aims to identify facial landmarks that are the key distinguishing factors of the face. The key factors can be the distance between the eyes, the depth of the eye sockets, the shape of the cheekbones, etc.
2.Encode
In the third step, information gathered in the second step is converted into numbers. The face analysis of an image gets transformed into a mathematical formula. This numerical value is often called faceprint. Just as every thumbprint is unique, each person has a unique faceprint as well.
3.Match
Finally, the target faceprint is compared against a database of other known faceprints. If a faceprint matches an image in a facial recognition database, then a match is made. Every application using facial recognition has its database and the match is determined using that database.
Scope of Deep Learning for Face Detection
The pandemic has got the world to move towards “contactless” and this leads to the mushrooming scope of deep learning. Here are some areas that define the span of deep learning for face detection:
- Marketing: Face detection has become indispensable for marketing, analyzing customer behavior, and personalized advertising.
- Crowd Surveillance: Deep learning is used to detect crowds in commonly used public or private areas.
- Banking and Telecom: Biometric online banking leaves no room for hackers to commit fraud.
- Healthcare: Many healthcare companies use face detection for comprehensive patient care. Patients’ records, history, genetic conditions, medications, etc. can be streamlined with this technology.
- Missing persons: Face detection does wonder in finding missing people and the victims of human trafficking.
- Retail: The deep learning technology offers improved retail experiences for customers. Contactless kiosks, Face-pay, personalized suggestions, etc.
- Reducing Crimes: Face detection helps to automatically ban the entry of shoplifters, organized retail criminals, or people with a history of fraud.
- Attendance: Many companies have stopped using finger biometrics for punching attendance during the pandemic. Facial recognition is a sophisticated way of marking attendance.
Apart from the above, innumerable industries are opting for facial recognition offering tremendous scope for deep learning.
Conclusion
Deep learning is a ubiquitous aspect that is growing due to its supremacy in terms of speed and accuracy. Covid-19 outbreak propelled the companies to get rid of traditional biometrics systems and adopt the latest facial recognition techniques.
The country’s increasing interest in artificial intelligence (AI) and data makes it a top career choice. Open the horizons of your career and solve a wide range of reflexive and cognitive problems by learning this state-of-the-art technology
