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Face Detection. Lecturers: Mor Yakobovits Roni Karlikar. Supervisor: Hagit Hel-Or. Introduction. Humans can easily detect faces, although faces can be very different from each other. Humans have also tendency to see face patterns even if they don’t really exist. Faces everywhere.

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## Face Detection

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**Face Detection**Lecturers: MorYakobovits RoniKarlikar Supervisor: Hagit Hel-Or**Introduction**Humans can easily detect faces, although faces can be very different from each other.**Humans have also tendency to see face patterns even if they**don’t really exist.**Faces everywhere**http://www.marcofolio.net/imagedump/faces_everywhere_15_images_8_illusions.html**Face Detection**• The problem of face detection is:Given an image, say if it contains faces or not. • The idea of face detection in computer vision is to let the computer learn to detect faces in images, just as a human can do.**Applications of Face Detection**• Auto-focus in cameras • Security systems (recognize faces of certain people) • Human-computer interface • Marketing systems • Much more..**Difficulties of Face Detection**Building a model for faces is not a simple task, faces can be complex and vary from each other. Faces in images are also affected from the environment.**Difficulties - Changing lightening**• Affects color, facial feature**Difficulties - Skin Tone**• Large variety of skin tones.**Difficulties - Facial Expressions**• Affects shape of face and its features**Difficulties - Obstructions**• Obstruction of facial features**Today’s Lecture**• We will talk about: • Skin detection • Eigenfaces • Viola-Jones algorithm**Today’s Lecture**• All of the 3 approaches we’ll see today are based on learning. • The computer learns to detect faces.**Learning - Intro**• The learning model we’ll use is Classifier. • Purpose: classify data to several classes. • Training level: let the computer learn the features of each class (face & non-face). This is done using a dataset with examples for instances of each class. (the instances are already classified) • Classification: given a new instance, tell which class it belongs. Example: Studying for exam by solving previous exams.**Face Detection Using Skin Detection**Probabilistic Approach image source**Skin Detection**• Purpose: • Find “skin pixels” in a given image. • The main question: • How to determine if a pixel is a “skin pixel”? • Our approach will be to teach the computer what color is a skin color and what color isn’t.**Skin Detection**• Skin detection is a color(pixel) – based approach for detecting faces. • This approach is quite simple • but has limited results due to • high sensitivity to illumination and other changes in skin tones • not only faces contain skin (arms, legs…) • some objects have similar colors to skin (for example, wooden furniture)**a yellow-biased face image (b) a light-compensated image**(c) skin regions of (a) in white (d) skin regions of (b) Example for how illumination cause false-negative and false-positive detection. “Detecting Faces in Color Images” by Hsu, Abdel-Mottaleb & Jain**Another examples for false-positive (chair in top left**corner) & false-negative (dark area of face in the image of the soccer player) skin detections. Rehg & Jones (1999)**Skin Colors In RGB Color Space**97% of the skin-color bins overlaps with non-skin color bins. explanation could be - many objects whose color resemble a skin color, like walls, railtracks, furnitures and wooden objects. Rehg & Jones (1999)**Skin Classifier**• The problem: • Given a pixel x with color (r,g,b), determine if it’s a skin or not. skin**Skin Classifier**• Given x = (R,G,B), how do we determine it’s class? (skin/non-skin) • Nearest neighbor • find labeled pixel closest to X • choose the class that pixel • Data modeling • fit a model (curve, surface, or volume) to each class • Probabilistic data modeling • fit a probability model to each class • we’ll focus on this approach Orange dots – skin Purple dots – non skin source**Probabilistic Skin Classifier**• Two approaches we’ll discuss • Gaussian-Based (parametric model) • Histogram-Based (non-parametric model)**Parametric modeling**Main Idea: • Assume the type/shape of the distribution we’re trying to find. • Find the parameters values for the assumed type from a training set.**Gaussian-Based Approach**• (Parametric model) • Single Gaussian Model • We assume the probabilistic distribution we are trying to find is a Normal Distribution (Gaussian function) • To find that distribution, all we need is: • - mean of the learned skin colors • - covariance matrix of the learned skin colors is a color vector! Those parameters are evaluated separately for each class**Gaussian-Based Approach**• (Parametric model) • After we have the mean & covariance, we get: • Where is the mean vector and is the covariance matrix of class j • For j=skin & j=non-skin**What we have**• P(rgb / skin) & P(rgb / ~skin) – “probability that a (non-)skin pixel will have the color rgb” But that’s not what we want.**What we need**• We need P(skin / rgb) & P(~skin / rgb) – “the probability that a pixel with the color rgb is (non-) skin” Remember Bayes’ Rule? After we achieve that, we can use MAP estimation.**Bayes’ Rule**P(skin) is the portion of skin pixels from total pixels in the learning dataset P(R) can be calculated using the probabilities we already have.**MAP Estimation**(Maximum A Posteriori estimation) Classification: A pixel with color R will be classified as skin iff P(skin / R) is higher than P(~skin / R) • MAP estimation- • Maximizes the probability for the posterior, and so • Minimizes the probability for false-negative misclassification • False-negative misclassification: a skin pixel classified as non-skin**Another Gaussian-Based Approach**• (Parametric model) • Problem with Single Gaussian Model: • Actual skin distribution might be too complex to be represented as a Gaussian distribution. • Solution: Mixture of Gaussians (MoG) • Represent the distribution with several different Gaussian distributions to allow more dynamic modeling of the distribution**Skin Color distribution in HSV color space**• HSV (Hue, Saturation, Value) • separates color components from intensity • (in RGB intensity affects all channels) • - Not the best color space for color-based approaches, but conversion is very simple compared to the better color spaces source**Gaussian-Based Approach**• (Parametric model) • Drawback: • Slower learning because we need to use EM algorithm to estimate the MoG • Slower classification, since it requires a evaluation all of the Gaussians • In the case of Mixture of Gaussians: Classification: Use Bayes’ and then MAP**MoG vs. Single Gaussian**Single Gaussian Training set distribution MoG source**Non-parametric modeling**Main Idea: • Do not assume anything about the distribution we are looking for. • Derive the distribution directly from the dataset.**Histogram-Based Approach**• Non-parametric model • Learn from a labeled dataset • for each color bin (256*256*256 ~ 16.7m in RGB), count • how many pixels of that color were skin • how many pixels of that color were non-skin • We get a histogram: (Our histograms will have three dimensions) And a equivalent histogram for non-skin pixels.**Histogram-Based Approach**• Non-parametric model • we have P(rgb / skin) & P(rgb / ~skin) • we need P(skin / rgb) & P(~skin / rgb)**Histogram-Based Approach**• Non-parametric model • A 3D histogram looks like this: • Viewing direction along the green-magenta axis which joins corners (0,255,0) and (255,0,255) in RGB • The viewpoint was chosen to orient the gray line horizontally • 8 bins in each color channel • Only shows bins with counts greater than 336,818 Rehg & Jones (1999)**Histogram-Based Approach**• Non-parametric model • Step by step explanation: • Learning: • Using a labeled dataset: • For each color X: count how many occurrences of X as skin pixel & non-skin pixels: • Normalize each histogram for each color X: & respectively**Histogram-Based Approach**• Non-parametric model • Step by step explanation: (cont’d) • Learning: 3. Apply bayes’ rule, for each color X: • We have P(X|skin) from histogram N • .P(skin) = • . • P(X) = • Symmetrically for**Histogram-Based Approach**• Non-parametric model • Step by step explanation: (cont’d) • Classification: • We are given a color X • Determine class with: (MAP estimation) • Only 2 table look-ups! • One in the skin histogram and one in the non-skin histogram**Histogram-Based Approach - Example**• Non-parametric model • Assume we observed from the dataset: • 534 skin-pixels with the color (100, 100, 100) • 330 non-skin pixels with the color (100, 100, 100) • Total number of observed pixels is 10000 • 5000 skin pixels • 5000 non-skin pixels • We get the corresponding probabilities: • P((100, 100, 100)| Skin ) = 534/5000= 0.1068 • P((100, 100, 100)| Non-skin ) = 330/5000= 0.066 • The histograms (skin & non-skin):**Example – cont’d**Reminder: Bayes’ Rule - • P((100, 100, 100)| Skin ) = 0.1068 • P((100, 100, 100)| ~Skin ) = 0.066 • Using Bayes’ Rule: • P((100, 100, 100) | Skin ) is bigger than P((100, 100, 100) | ~Skin ) • And so every pixelwith the color(100, 100, 100) will be classified as a skin pixel**Bibliography**• Statistical Color Models with Application to Skin Detection by Rehg & Jones (1999) • “Detecting Faces in Color Images” by Hsu, Abdel-Mottaleb & Jain (2002) • “A Survey on Pixel-Based Skin Color Detection Techniques” byVezhnevets, Sazonov & Andreeva (2003) • http://alumni.media.mit.edu/~maov/classes/comp_photo_vision08f/lect/05_skin_detection.pdf • http://pages.cs.wisc.edu/~lizhang/courses/cs766-2007f/syllabus/10-23-recognition/10-22-recognition.ppt**Eigenfaces**M.A. Turk and A.P. Pentland: Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3 (1):71--86, 1991.

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