Face recognition technology

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Face Recognition Technology

Presented By:Ranjit R, Banshpal

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S.R.B GROUP OF INSTITUTIONS

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INDEX

Biometrics Face recognition Different approaches ML Algorithm Proposed System Software used in face recognition Technology Advantage Application Conclusion References

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“A biometric is a physiological or behavioral characteristic of a human being that can distinguish one person from another and that theoretically can be used for identification or verification of identity.”

WHAT IS BIOMETRICS ?

Biometric applications available today are categorized into 2 sectors Psychological: Iris, Fingerprints, Hand, Retinal and Face recognition Behavioral: Voice, Typing pattern, Signature

Face RecognitionFace recognition systems (FRSs) are an important field in computer vision, because it represent a non-invasive BI technique.

1. A face detection algorithm is used for extracting faces from video frames (training videos) and generating a face database.

2. Filtering and preprocessing are applied to face images obtained in the previous step.

3. A collection of machine learning algorithms are trained using as input data the faces obtained in the previous step.

4. Finally, the classifiers are used for classify faces obtained from video frames

Facial recognition is a form of computer vision that uses faces to attempt to identify a person or verify a person’s claimed identity. For face recognition there are two types of comparisons,

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1) IDENTFICATION- figure out “Who is X?”- accomplished by system performing a “one-to-

many ” search

2) VERIFICATION- answer the question “Is this X?”- accomplished by the system performing a “one-to-

one” search

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DIFFERENT APPROACHES Describe the different methods of face recognition.

Feature extraction methods The input image to identify and extract (and measure) distinctive facial features such as the eyes, mouth, nose, etc. Compute the geometric relationships among those facial points, thus reducing the input facial image to a vector of geometric features

Holistic methods Holistic approaches attempt to identify faces using global representations, i.e., descriptions based on the entire image rather than on local features of the face

ML Algorithms During the past decades, several ML algorithms have been proposed for classification tasks. Most of them are from the theoretical view under some assumption about data distribution, characteristics of the classification task, signal to-noise-ratio, etc. In reality, these assumptions are often hard to be verified. Therefore, a practical solution for selecting an appropriate model for a given classification task is to experimentally compare these algorithms. Five widely used machine classifiers

K-Nearest Neighbor (KNN) Locally-Weighted Learning (LWL) Naive Bayes classifier (NB) Decision Table Classifier (DT) Single Decision Tree (SDT).

Single Decision Tree (SDT) :

Decision tree induction is the learning of decision trees from class-labeled training tuple.

A decision tree is a flowchart-like tree structure.

Each internal node (non leaf node) denotes a test on an attribute.

Each branch represents an outcome of the test.

Each leaf node (or terminal node) holds a class label.

The topmost node in a tree is the root node.

A path is traced from the root to a leaf node.

Single Decision Tree (SDT) :

Most algorithms for decision tree induction follow a top-down approach.

Starts with a training set of tuples and their associated class labels.

The training set is recursively partitioned into smaller subsets as the tree is being built.

To split D into smaller partitions according to the outcomes of the splitting criterion.

The specific algorithm for generating the decision tree is called C4.5 algorithm.

Consider the two different videos of 10-second duration were used. A total of 10x30x2 = 600 frames where processed. In the input video, there was 6 different individuals, representing a total of 3, 600 samples (600 for each individual). Three versions of the dataset were generated: one for a 100 x 100 pixels face resolution, one for a 50 x 50 pixels face resolution, and finally one for a 25 x 25 pixels face resolution

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Proposed System

Face Detection Face detector implemented on OpenCV. Faces were detected using the function cvHaarDetectObjects. The Semi-Aided Labeling Module (SALM) reads the input video,

and for each frame where at least one face was detected by the face detection module.

Filtering and Preprocessing (FPM) This module performs the following transformations:

RGB to Gray scale Transformation: For reducing the amount of data to be processed, a 24-bit per pixel RGB format is transformed into a 8-bit per pixel gray-scale format.

Scaling: The face images are scaled to a fixed number of rows and columns. The output resolution for each face can be set by user according to the required accuracy.

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Tabular Dataset Building Module (TDBM) This module obtains the image pixels, and generates a tabular

dataset. where rows are the total number of subjects, and the columns are

the image pixels. The final column represents the class attribute.

TrainingFor performing the training of the classification algorithms, the following steps are required:

Permute and split dataset. This operation is performed by the Random Permutation and Splitting Module (RPSM). Basically, a random permutation of the samples contained in the tabular dataset is performed, and the resulting dataset is divided into two datasets: the training dataset and the test dataset.

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Train classification algorithms. Each classification algorithm takes as input the training data set generated by the RPSM, and performs the model building for each classifier. Later, the model for each classifier is stored in disk for use it later in the classification step.

Classification In this module, with the help of the previous trained classifiers,

takes as input the faces from the test set, applies filter and pre-preprocessing operators, and evaluates the test face in each model generated by the trained classifiers.

After doing this comparison, face image is classified with the label or name predicted by each classified.

The output of each classified is processed by the Performance Evaluation Module (PEM), which generates a table with a comparison among several classifiers.

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SOFTWARE USED IN FACE RECOGNITION TECHNOLOGY

Facial recognition software falls into a larger group of technologies known as biometrics. Here is the basic process that is used by the Face system to capture and compare images

Detection When the system is attached to a video surveillance system, the recognition software searches the field of view of a video camera for faces.

If there is a face in the view, it is detected within a fraction of a second. A multi-scale algorithm is used to search for faces in low resolution. Alignment Once a face is detected, the system determines the head's position, size and pose. A face needs to be turned at least 35 degrees toward the camera for the system to register it. Normalization Normalization is performed regardless of the head's location and distance from the camera. Light does not impact the normalization process. Representation The system translates the facial data into a unique code. This coding process allows for easier comparison of the newly acquired facial data to stored facial data.

Matching The newly acquired facial data is compared to the stored data and linked to at least one stored facial representation. This is the mathematical technique the system uses to encode faces. The system can match multiple face prints at a rate of 60 million per minute from memory or 15 million per minute from hard disk. The comparison using a scale of one to 10. If a score is above a predetermined threshold, a match is declared.

ADVANTAGES

Convenient, social acceptability Easy to use Inexpensive technique of identification

APPLICATIONS1. Replacement of PIN

2. Border control

3. Voter verification

4. Computer security

5. Government Use, a. Security/Counterterrorism. b. Immigration 8. Commercial Use, a. Residential Security b. Banking using ATM

This technique evaluate the suitability of both computer vision and ML techniques for solving the problem of face detection and recognition. Face recognition technologies have been associated generally with very costly top secure applications. Today the core technologies have evolved and the cost of equipment’s is going down dramatically due to the integration and the increasing processing power.

CONCLUSION

References

1. E. Garc´ ıa Amaro, M.A. Nu ˜ no-Maganda and M. Morales-Sandoval, “Evaluation of Machine Learning Techniques for Face Detection and Recognition”, IEEE 2012.

2. Claudia Iancu, Peter Corcoran and Gabriel Costache,” A Review of Face Recognition Techniques for In-Camera Applications”, IEEE 2007.

3. Brian C. Becker, Enrique G.Ortiz, “Evaluation of Face Recognition Techniques for Application to Facebook ” 2008 IEEE

4. D. Bhattacharyya, R. Ranjan, F. Alisherov, and M. Choi, “Biometric authentication: A review,” International Journal of u- and e- Service, Science and Technology, vol. 3, no. 2, pp. 23–27, 2009.

5. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006

6. G. Bradski and A. Kaehler, Learning OpenCV. O’Reilly Media Inc., 2008.

Thank you…