Real-time Embedded Face Recognition for Smart Home Fei Zuo, Student Member, IEEE, Peter H. N. de...
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Transcript of Real-time Embedded Face Recognition for Smart Home Fei Zuo, Student Member, IEEE, Peter H. N. de...
Real-time Embedded Face Recognition for Smart Home
Fei Zuo, Student Member, IEEE, Peter H. N. de With, Senior Member, IEEE
IntroductionIntroduction
Consumer electronic devices can sense and understand their surroundings and adapt their services according to the contexts.
Advantageous of face recognition
Nonintrusive and user-friendly interfaces:
Low-cost sensors and easy setup
Active identification
The challenges in consumer applications
Large variability of operating environments (e.g. illumination and backgrounds).
Processing efficiency with low-cost consumer hardware.
Nearly unconstrained capturing of facial images.
ARCHITECTURE OVERVIEW
The embedded HomeFace system consists of
kernel component: Performing the face detection/recognition.
interfacing component: Providing a uniform interface to different hosti
ng devices.
Kernel component Kernel component processing pipelineprocessing pipeline
Face detection ( attention capture ) Feature extraction for face normalization (p
reprocessing for classification) Face identification
PipelinePipeline can be executed on the centralized mode or the distributed mode
Processing flow
Image processing algorithm architecture
FACE DETECTION
Using a detector cascade to build a face detector that is highly efficient and robust
Advantages of detector cascade: 1. Various image features are used 2. Largely reduces the overall computation cost 3.Retaining high detection accuracy.
Color-based face detector
Coarsely locating potential facial regions by using
color space from a condensed skin-color cluster.
Fitting a convex hull to the skin-color cluster in the
plane.
b rYC C
b rC C
Color-based face detector
Apply a binary majority filter as a post-processor to smooth the segmentation result
Geometry-based face detector
Algorithm 1: The geometry-based face verification.1. Generate the vertical profile of the candidate region2. Select local minima of the profile as candidate vertical
locations of eyes and mouth. If no proper minima are found, return non-face;
3. For each candidate vertical location, a sliding window is applied to search horizontally for the most probable eye-pair (or mouth). The average region intensity is used as a fast evaluation criterion. If the lowest average intensity is above a threshold, return non-face;
4. Check whether the selected feature group (eyes + mouth) forms an approximate equilateral triangle. If not, return non-face.
Geometry-based face detector
Learning-based detector
For the final detector in the cascade, a neural-network-based(NN) detector is used.
Its purpose is the final verification of facial regions
Facial feature extraction and face normalization
The direct use of it will potentially lead to identification failures.
We propose a two-step coarse-to-fine feature extractor
1.Edge Orientation Matching (EOM):
2.H-ASM (an enhanced version of ASM)
Feature estimation by EOM
Using 3×3 Sobel edge filter
Edge-Strength image ( ES ) Edge-Orientation image ( EO )
Feature estimation by EOM Matching function between two image regions P1 and P2 is de
fined as
Using the average ES and EO as a template, a multiresolution search is performed over the detected facial region for the position and scale yielding the best match
Feature estimation by EOM
Deformable shape fitting by H-ASM
1) Facial feature model with enhanced Haar textures
2) Fast computation of Haar textures
3) Haar feature selection
4) Haar feature weighting
5) Feature extraction by H-ASM
Facial feature model with enhanced Haar textures
Build a facial feature model as an ordered set of Nf feature points.
iWe model by extracting an block around FiT M M ��������������
iT is Subsequently transformed using Haar-transform
Facial feature model with enhanced Haar textures
Fast computation of Haar textures
Haar decomposition mainly involves summations of pixel sub-blocks, which can be efficiently computed by using two auxiliary ‘integral images’
The illumination normalization for each block (by zero mean and one standard deviation) can be conveniently integrated into the computation of Haar coefficients.
Haar feature selection
Haar feature weighting
Feature extraction by H-ASM
From the initial estimation of the feature position, an initial shape model can be overlayed to the real image
Face normalization Using an affine transformation to warp an input face
with varying scale, position and pose to a standard frame.
feature locations are , where
, and the feature locations of a standard face are ,
Face Identification
In space In space Φ (Linear Discriminant Analysis)
Face Identification
EXPERIMENTAL RESULTS
EXPERIMENTAL RESULTS
EXPERIMENTAL RESULTS
Database composed of 25 peopleDatabase composed of 25 people Each person has 4 to 8 sample picturesEach person has 4 to 8 sample pictures Test under a variety of environmentsTest under a variety of environments HomeFace system achieves a total recognition HomeFace system achieves a total recognition
rate of 95%.rate of 95%. 3-4 frames/second processing speed on a P-IV 3-4 frames/second processing speed on a P-IV
PC(1.7GHz) in centralized modePC(1.7GHz) in centralized mode
EXPERIMENTAL RESULTS