A Hand Gesture Recognition System Based on Local Linear Embedding
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A Hand Gesture Recognition System Based on Local Linear Embedding
Introduction
 Interaction with computers are not comfortable experience
 Computers should communicate with people with body language.
 Hand gesture recognition becomes important
 Interactive human-machine interface and virtual environment
 Two common technologies for hand gesture recognition
 glove-based method
 Using special glove-based device to extract hand posture
 Annoying
 vision-based method
 3D hand/arm modeling
 Appearance modeling
 3D hand/arm modeling
 Highly computational complexity
 Using many approximation process
 Appearance modeling
 Low computational complexity
 Real-time processing
Overview of algorithm proposed in the paper
 Vision-based method to be used for the problem of CSL real-time recognition
 Input: 2D video sequences
 two major steps
 Hand gesture region detection
 Hand gesture recognition
 CSL and Pre-processing
 Sign Language
 Rely on the hearing society
 Two main elements:
 Low and simple level signed alphabet, mimics the letters of the native spoken language
 Higher level signed language, using actions to mimic the meaning or description of the sign
CSL and Pre-processing
 CSL is the abbreviation for Chinese Sign Language
 30 letters in CSL alphabet ßà Objects in recognition
Pre-processing of Hand Gesture Recognition
 Detection of Hand Gesture Regions
 Aim to fix on the valid frames and locate the hand region from the rest of the image.
 Low time consuming à fast processing rate à real time speed
Pre-processing of Hand Gesture Recognition
 Detect skin region from the rest of the image by using color.
 Each color has three components
 hue, saturation, and value
 chroma consists of hue and saturation is separated from value
 Under different condition, chroma is invariant.
 Color is represented in RGB space, also in YUV and YIQ space.
 In YUV space
 saturation à displacement
 hue -> amplitude
 In YIQ space
 The color saturation cue I is combined with Θto reinforce the segmentation effect
 Skins are between red and yellow
 Transform color pixel point P from RGB to YUV and YIQ space
 Skin region is:
 105 º <= Θ<= 150 º
 30 <= I <= 100
 Hands and faces
 On-line video stream containing hand gestures can be considered as a signal S(x, y, t)
 (x,y) denotes the image coordinate
 t denotes time
 Convert image from RGB to HIS to extract intensity signal I(x,y,t)
 Based on the representation by YUV and YIQ, skin pixels can be detected and form a binary image sequence M’(x,y,t) – region mask
 Another binary image sequence M’’(x,y,t) which reflects the motion information is produced between every consecutive pair of intensity images – motion mask
 M(x,y,t) delineating the moving skin region by using logical AND between the corresponding region mask and motion mask sequence
 Normalization
 Transformed the detection results into gray-scale images with 36*36 pixels.
Locally Linear Embedding
 Sparse data vs. High dimensional space
 30 different gestures, 120 samples/gesture
 36*36 pixels
 3600 training samples vs. d = 1296
 Difficult to describe the data distribution
 Reduce the dimensionality of hand gesture images
 Locally Linear Embedding maps the high-dimensional data to a single global coordinate system to preserve the neighbouring relations.
 Given n input vectors {x1, x2, …, xn},
è LLE algorithm
è {y1, y2, …, yn} (m<<d)
 Find the k nearest neighbours of each point xi
 Measure reconstruction error from the approximation of each point by the neighbour points and compute the reconstruction weights which minimize the error
 Compute the low-embedding by minimizing an embedding cost function with the reconstruction weights
Experiments
 4125 images including all 30 hand gestures
 60% for training , 40% for testing
 For each image:
 320*240 image, 24b color depth
 Taken from camera with different distance and orientation
 Sampled at 25 frames/s
 Experiment Results
Conclusion
 Robust against similar postures in different light conditions and backgrounds
 Fast detection process, allows the real time video application with low cost sensors, such as PC and USB camera
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