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Amit Jain
- Software Developer
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Face detection is a technology that is used to identify a human face in digital images. This technique can be regarded as a specific case of Object-Class Detection where the main task is to find the locations and sizes of all objects in an image that belongs to a given class. For example Torsos, Pedestrians & Cars.
Face Detection Algorithms mainly focus on the identification of frontal human faces. It is quite similar to Image Detection where the Image of a person is matched bit by bit. The image is matched by the image stored in a database. So any facial feature changes in the database will invalidate the matching process.
"The basic and most reliable Face Detection approach is based on two Algorithms namely Genetic Algorithm & Eigenface Algorithm."
First of all, the human eye regions are detected by testing all the valley regions of the Gray-Level Image. After that, the Genetic Algorithm is used to generate all the possible face regions that include the Nose, the Mouth corners, the Eyebrows.
Each possible face candidate is normalized to reduce both the Lighting Effect (which is caused by the uneven illumination) and the Shirring Effect (which is caused due to the movement of the head).
The fitness value of each candidate is measured based on its projection on the Eigenfaces. Then after a number of iterations, all the face candidates with a high fitness value are selected for the verification in future. Now at this stage, the face symmetry is measured and the existence of the different facial features I verified for each face candidate.
APPLICATIONS
Facial Recognition:
Face Detection is used in Biometrics, and sometimes as a part of a Facial recognition System.
Photography:
Some of the recent Digital Cameras uses this technology of Face detection for AUTOFOCUS. This is also useful in the field of Video Surveillance. Also, the Human-Computer Interface and Image Database Management use this technology.
Marketing:
The Face Detection technology is also gaining the interest in the marketing field. A Webcam can be integrated into a Television and can identify any face that walks by. Afterward, the system then calculates the Gender, Age range and Race of the face. Once the information is collected, a series of advertisements can be played that is specific toward the identified Race, Gender, and Age.
Face Detection Algorithms mainly focus on the identification of frontal human faces. It is quite similar to Image Detection where the Image of a person is matched bit by bit. The image is matched by the image stored in a database. So any facial feature changes in the database will invalidate the matching process.
"The basic and most reliable Face Detection approach is based on two Algorithms namely Genetic Algorithm & Eigenface Algorithm."
First of all, the human eye regions are detected by testing all the valley regions of the Gray-Level Image. After that, the Genetic Algorithm is used to generate all the possible face regions that include the Nose, the Mouth corners, the Eyebrows.
Each possible face candidate is normalized to reduce both the Lighting Effect (which is caused by the uneven illumination) and the Shirring Effect (which is caused due to the movement of the head).
The fitness value of each candidate is measured based on its projection on the Eigenfaces. Then after a number of iterations, all the face candidates with a high fitness value are selected for the verification in future. Now at this stage, the face symmetry is measured and the existence of the different facial features I verified for each face candidate.
APPLICATIONS
Facial Recognition:
Face Detection is used in Biometrics, and sometimes as a part of a Facial recognition System.
Photography:
Some of the recent Digital Cameras uses this technology of Face detection for AUTOFOCUS. This is also useful in the field of Video Surveillance. Also, the Human-Computer Interface and Image Database Management use this technology.
Marketing:
The Face Detection technology is also gaining the interest in the marketing field. A Webcam can be integrated into a Television and can identify any face that walks by. Afterward, the system then calculates the Gender, Age range and Race of the face. Once the information is collected, a series of advertisements can be played that is specific toward the identified Race, Gender, and Age.
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hope this helps you
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well you can use dlib with python. visit :http://pythongeeks.net/face-landmark-detection-using-python-and-dlib/ for a sample or you can go with LBP or even opencv's haar cadcade: http://pythongeeks.net/face-and-eyes-detection-using-python-and-opencv-haar-cascades/
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