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![Knowledge-Based Method: [Yang and Huang 94] Level 1 (lowest resolution): apply the rule “the center part of the face has 4 cells with a basically uniform intensity” to search for candidates Level 2: local histogram equalization followed by edge equalization followed by edge detection Level 3: search for eye and mouth features for validation](https://image.slidesharecdn.com/enggroomcodeintrofacedetectrecognition-241117054638-74274a10/75/EnggRoom_Code_IntroFaceDetectRecognition-ppt-9-2048.jpg)
![Face and Non-Face Exemplars Positive examples: Get as much variation as possible Manually crop and normalize each face image into a standard size(e.g., 19×19 Creating virtual examples [Poggio 94] Negative examples: Fuzzy idea Any images that do not contain faces A large image subspace Bootstraping[Sung and Poggio 94]](https://image.slidesharecdn.com/enggroomcodeintrofacedetectrecognition-241117054638-74274a10/75/EnggRoom_Code_IntroFaceDetectRecognition-ppt-16-2048.jpg)
EnggRoom_Code_IntroFaceDetectRecognition.ppt
- 1. An Introduction toFace Detection and Recognition Ziyou Xiong Dept. of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign
- 2. Outline Face Detection Whatis face detection? Importance of face detection Current state of research Different approaches One example Face Recognition What is face recognition? Its applications Different approaches One example A Video Demo
- 3. What is FaceDetection? Given an image, tell whether there is any human face, if there is, where is it(or where they are).
- 4. Importance of Face Detection The first step for any automatic face recognition system system First step in many Human Computer Interaction systems Expression Recognition Cognitive State/Emotional State Recogntion First step in many surveillance systems Tracking: Face is a highly non rigid object A step towards Automatic Target Recognition(ATR) or generic object detection/recognition Video coding……
- 5. Face Detection: currentstate State-of-the-art: Front-view face detection can be done at >15 frames per second on 320x240 black-and-white images on a 700MHz PC with ~95% accuracy. Detection of faces is faster than detection of edges! Side view face detection remains to be difficult.
- 6. Face Detection: challenges Out-of-Plane Rotation: frontal, 45 degree, profile, upside down Presence of beard, mustache, glasses etc Facial Expressions Occlusions by long hair, hand In-Plane Rotation Image conditions: Size Lighting condition Distortion Noise Compression
- 7. Different Approaches Knowledge-basedmethods: Encode what constitutes a typical face, e.g., the relationship between facial features Feature invariant approaches: Aim to find structure features of a face that exist even when pose, viewpoint or lighting conditions vary Template matching: Several standard patterns stored to describe the face as a whole or the facial features separately Appearance-based methods: The models are learned from a set of training images that capture the representative variability of faces.
- 8. Knowledge-Based Methods TopTop-down approach: Represent a face using a set of human-coded rules Example: The center part of face has uniform intensity values The difference between the average intensity values of the center part and the upper part is significant A face often appears with two eyes that are symmetric to each other, a nose and a mouth Use these rules to guide the search process
- 9. Knowledge-Based Method: [Yang andHuang 94] Level 1 (lowest resolution): apply the rule “the center part of the face has 4 cells with a basically uniform intensity” to search for candidates Level 2: local histogram equalization followed by edge equalization followed by edge detection Level 3: search for eye and mouth features for validation
- 10. Knowledge-based Methods: Summary Pros: Easy to come up with simple rules Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified Work well for face localization in uncluttered background Cons: Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases
- 11. Feature-Based Methods Bottom-upapproach: Detect facial features (eyes, nose, mouth, etc) first Facial features: edge, intensity, shape, texture, color, etc Aim to detect invariant features Group features into candidates and verify them
- 12. Feature-Based Methods: Summary Pros:Features are invariant to pose and orientation change Cons: Difficult to locate facial features due to several corruption (illumination, noise, occlusion) Difficult to detect features in complex background
- 13. Template Matching Methods Store a template Predefined: based on edges or regions Deformable: based on facial contours (e.g., Snakes) Templates are hand- coded (not learned) Use correlation to locate faces
- 14. Template-Based Methods: Summary Pros: Simple Cons: Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledge- based methods)
- 15. Appearance-Based Methods: Classifiers Neuralnetwork Multilayer Perceptrons Princiapl Component Analysis (PCA), Factor Analysis Support vector machine (SVM) Mixture of PCA, Mixture of factor analyzers Distribution Distribution-based method Naïve Bayes classifier Hidden Markov model Sparse network of winnows (SNoW) Kullback relative information Inductive learning: C4.5 Adaboost …
- 16. Face and Non-Face Exemplars Positive examples: Get as much variation as possible Manually crop and normalize each face image into a standard size(e.g., 19×19 Creating virtual examples [Poggio 94] Negative examples: Fuzzy idea Any images that do not contain faces A large image subspace Bootstraping[Sung and Poggio 94]
- 17. Exhaustive Search Acrossscales Across locations
- 18. Theory of OurAlgorithm
- 19. Theory of OurAlgorithm(2)
- 20. Theory of OurAlgorithm(3)
- 21. Instance of the"Travelling Salesman Problem"
- 22. Intuition of Permutation When modelling face images as a k-th order Markov process, rows of the images are concatenated into long vectors. The pixels corresponding to the semantics(e.g, eyes, lips) will be scatted into different parts in the vectors. The Markovian property is not easy to be justified. If some permutation can be found to re-group those scattered pixels(i.e, to put all the pixels corresponding to eyes together, those for lips together), then the Markov assumption is more reasonable.
- 23. Preprocessing Rotation Scaling Quantizing
- 24. Facial Features Detection Region search
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- 26. Face and FacialFeature Detection The algorithm is also used to detect 9 facial features: 2 outer mouth corners, 2 outer eye corners, 2 outer eye-brow corners, 2 inner eye-brow corners and the center of the nostrils.
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- 33. Appearance-Based Methods: Summary Pros: Use powerful machine learning algorithms Has demonstrated good empirical results Fast and fairly robust Extended to detect faces in different pose and orientation Cons: Usually needs to search over space and scale Need lots of positive and negative examples Limited view-based approach
- 34. Color-Based Face Detector Pros: Easy to implement Effective and efficient in constrained environment Insensitive to pose, expression, rotation variation Cons: Sensitive to environment and lighting change Noisy detection results (body parts, skin-tone line tone line regions)
- 35. What is FaceRecognition? A set of two task: Face Identification: Given a face image that belongs to a person in a database, tell whose image it is. Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database.
- 36. Difference between Face Detectionand Recognition Detection – two-class classification Face vs. Non-face Recognition – multi-class classification One person vs. all the others
- 37. Applications of Face Recognition Access Control Face Databases Face ID HCI - Human Computer Interaction Law Enforcement
- 38. Applications of Face Recognition Multimedia Management Security Smart Cards Surveillance Others
- 39. Different Approaches Features: Featuresfrom global appearance Principal Component Analysis(PCA) Independent Component Analysis(ICA) Features from local regions Local Feature Analysis(LFA) Gabor Wavelet Similarity Measure Euclidian Distance Neural Networks Elastic Graph Matching Template Matching …
- 40. The PCA Approach- Eigenface The theory
- 41. The PCA Approach- Eigenface Eigenfaces – an example
- 42. Face Detection + Recognition Detection accuracy affects the recognition stage Key issues: Correct location of key facial features(e.g. the eye corners) False detection Missed detection
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