Face detection and recognition technology is very well known for identifying a person from a video clip or image. This is done using many ways like comparing facial features, using neural network or using eiganfaces.
Face detection and recognition has many applications in a variety of fields such as security system, videoconferencing and identification however face detection is not 100% accurate most of the times. We will talk about the drawbacks of Face identification and Recognition later in this post.
A face authentication system based on principal component analysis and neural networks consist of three stages; pre-processing, principal component analysis, and recognition.
- In pre-processing stage, normalization illumination, and head orientation are done.
- Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto the space expanded by eigenfaces and represented by weighted sum of the eigenfaces. These weights are used to identify the faces.
- In the third step, Neural network is used to create the face database. Recognition and authentication of the face is done by using the weights generated by eigenfaces.
Eigenfaces is the name given to a set of eigenvectors. Eigenface provides an easy way for face recognition as its training process is completely automatic and easy to code. Eigenface adequately reduces statistical complexity in face image representation. Eigenface can handle large databases and once eigenfaces of a database are calculated, face recognition can be achieved in real time.
Some of the well known software with face recognition ability include:
- iPhoto (Apple)
- Lightroom (Adobe)
- OpenCV (Open Source)
- Photos (Apple)
- Photoshop Elements (Adobe Systems)
- Picasa (Google)
- Picture Motion Browser (Sony)
- Windows Live Photo Gallery (Microsoft)
- FotoBounce (Applied Recognition Inc)
- DeepFace (Facebook)
Drawbacks with Face Detection and Recognition:
Face detection and recognition has failed many times because of various reasons like change of expressions, plastic surgery, angle at which picture is taken etc. Also, as the person grows old the saved data has no value.
When Face recognition was used to prevent public crimes, it failed completely as people were using masks to cover their faces.
Among the different biometric techniques, facial recognition may not be most reliable and efficient as compared to iris scan and finger scanning as these systems can generate accurate results. Having said that, Facial recognition has been used world wide during recent years mainly for security scan. Airports, stations, public places, crime detection – these are the major areas where Face detection and recognition is used. We hope that modern approaches will increase the accuracy in Face recognition and authentication.