How to Perform Face Recognition using Deep Learning?
The digital world we live in today has provided easy access to remote things and makes processes faster with online services.
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Updated November 2026. This was first published on 21 July 2021. The idea is the same. Any number below is what the original piece said then. It is not a new count.
Key takeaways
- The digital world we live in today has provided easy access to remote things and makes processes faster with online services.
- People need to open an account with the system services such as banks, social media, companies and other related services to get authorized access.
- These accounts are protected with passwords having alphabets, numbers, special characters and patterns.
The digital world we live in today has provided easy access to remote things and makes processes faster with online services. People need to open an account with the system services such as banks, social media, companies and other related services to get authorized access. These accounts are protected with passwords having alphabets, numbers, special characters and patterns. With in-depth knowledge, hackers can crack these passwords and get unauthorized access to private accounts. Moreover, physical threats and risks were always there to private, confidential, and personal data, assets and properties. To reduce these risks and dangers, face recognition systems were introduced about 60 years ago.
Face recognition has been applied practically in law enforcement, biometrics, access control, surveillance, information security and smart cards. Many companies prefer face recognition over other technologies as it removes physical interaction with the hardware, offers security & reliability, reduces maintenance cost and dependence on hardware. Moreover, it provides easy access to multiple locations and entities.
Face Recognition: #
Face recognition is identifying a person using a biometric method based on the face image. Biological traits are used to identify a person. A computerized method to perform face recognition was introduced due to the limited concentration span for human eyes. It includes automatic detection and verification of a person from an image or video.
Face detection: Locating faces in the image or video and mark them with a bounding box. Face alignment: Normalizing the face, like geometry and photometric, to make it consistent with the database. Feature extraction: Extracting face features that can be used to perform recognition tasks. Recognition task: Matching the test face against multiple known faces from the prepared/ available database.
Face recognition systems perform each step in a separate program or module or may have a single process integrated into a system. The face recognition task is broad and has specific needs of a prediction problem like face matching, similarity, and transformation. Face matching finds the best match of a given face. Face similarity checks face most similar faces, and face transformation generates new faces identical to the given face.
Feature-based face detection Image-based face detection.
Though face recognition is extensively researched, it still has challenges such as misalignment, illumination variation, pose variation and expression variation. To improve the accuracy and degree of precision while performing face recognition, multiple approaches are considered.
Face Detection With Deep Learning: #
Deep learning is capable of processing massive data and creating patterns that are applied in decision making. As a function in Artificial Intelligence and a subset of machine learning, deep learning algorithms have neural networks to learn unsupervised data. Deep learning methods use large datasets of faces. It allows deep neural networks to perform and outperform the face recognition what it can do of humans by learning and compacting face representations.
The face detection process is challenging due to different posture and light-shielding as it has a non-aligned and forcing environment. Recent research studies found that deep learning algorithms have the potential to solve these problems. Deep learning can approximate complex functions through the incremental hidden layers to separate and differentiate face recognition tasks. Among different neural network algorithms used in face detection, multi-task cascaded convolutional neural networks are most widely used.
Face Detection using Multi-Task Cascaded Convolutional Neural Network #
Face detection and alignment is based on face recognition, behavioral analysis, posture, visual impact and so forth. Previously proposed cascade face detection algorithms dropped while responding to significant face most similar faces, visual effect of face images. Existing dimension-based partitioning and merging clustering (DPM) algorithms have some defects. Still, Convolutional Neural Network (ConvNet/CNN) algorithms have given good results in visual terms like image classification, face recognition, and so forth. Previously developed frameworks had issues either in face detection or face alignment and were not capable of clearly differentiating both the tasks.
Multi-task Cascaded Convolutional Networks (MTCNN) is a framework developed to solve both face detection and face alignment problems in face recognition. The name is multi-task network as it includes three models, P-Net, R-Net and O-Net, trained on three different tasks. These three models are not connected directly. the previous stage’s output is given as an input to the next stage. It allows additional processing between the stages whenever required.
The MTCNN process has three stages of convolutional networks. These networks are used to recognize faces and landmark feature locations like nose, eyes, and mouth. First, the image is taken and resized to different scales to build an image pyramid. Then it is used as an input of the three-staged cascaded network of face detection.
Let’s see three stages of MTCNN in detail.
It is a fully convolutional network (FCN). It is used to gain candidate windows and bounding box regression vectors in the face image. Using this information, the localization of boxes is predicted while detecting an object from a pre-defined class. The bounding box vectors are refined to combine overlapping regions and candidate windows are obtained as an output.
The P-Net output is fed as an input to the Refine Network (R-Net). It is a CNN network as it has a dense layer at the last stage of the framework architecture. It overlaps the candidates using non-maximum suppression (NMS). It reduces the candidate numbers and calibrates the bounding box regression too. It predicts if the input is a face or not by localizing facial landmarks from the information. It is like a binary classification problem.
The Output Network (O-Net) describes the details in the face. As an output, it provides five facial landmark positions for (right and left) eye, nose and (left and right) mouth (corner).
Thus, MTCNN carries out three main tasks viz. Face classification, bounding box regression and facial landmark localization. Among the number of deep learning methods developed and demonstrated in the application of face detection, due to the capability of landmark detection, MTCNN is the most popular approach adopted. Though MTCNN architecture is complex to implement, open-source implementations of the architecture are available to train on new datasets and use pre-trained models for face detection.
The digital version of surveillance, authorization and authentication without directly handling hardware replaces previous digital versions of identification and verification. Face recognition is one of the oldest techniques to identify a person and its importance is increasing in almost all sectors. Face detection is a computer-vision problem of identifying and localizing faces in a given image. Different algorithms and methods are researched for face detection. deep learning based MTCNN network architecture has been the most popular face detection method. Due to cascaded models, open-source architectures, and datasets, face detection is achieved most accurately using the MTCNN library. So, MTCNN has prime importance in the face recognition industry.
Questions and answers #
What is the main point? #
The digital world we live in today has provided easy access to remote things and makes processes faster with online services.
What else did the original piece say? #
People need to open an account with the system services such as banks, social media, companies and other related services to get authorized access.