A real-time big data architecture for glasses detection using computer vision techniques

A Real-Time Big Data Architecture For
Glasses Detection Using Computer
Vision Techniques
Alberto Fernández, Rubén Casado, Rubén
Usamentiaga
1

OUTLINE
● Introduction
● Algorithm for glasses detection
● Big Data architecture for glasses detection
● Conclusions
● Future work
2Alberto Fernández - alberto.fernandez@fundacionctic.org

INTRODUCTION
● The rise of Internet, IoT and Cloud Computing has
led to an impressive growth of data
● Increasing information gathered by low-cost
information sensing devices
● Domain-specific information collected by
organizations should be analyzed automatically
3
LOGS SENSORS CAMERAS MICROPHONES
MOBILE
DEVICES
Alberto Fernández - alberto.fernandez@fundacionctic.org

INTRODUCTION
● SQL-based DB perfect for storing and processing
structured data but not prepared for Big Data
● Big Data is characterized by the 3Vs
○ Volume: the size of the data to be processed
○ Velocity: frequency of the data generation, dynamic
aspects of the data and generating the results in RT.
○ Variety: multimodal nature of data:
■ different data schemas of data source
■ structured data (ontologies)
■ unstructured data (sensors signals)

INTRODUCTION
● Big Data architectures can be classified into:
○ Batch processing (not real time)
■ Efficient way of processing high volumes of data
collected during a period of time.
■ Information collected into “batches” as a unit
○ Stream processing
■ Continuous input > processing > output data
■ low-response time achieved at the expense of less
rigorous analysis of data
○ Hybrid processing
■ Batch and stream processing results are required
■ Results are merged, synchronized and composed

INTRODUCTION
● Video/image generated by sensors and devices has
become the largest source
○ Processing surveillance videos for information extraction
requires real-time stream processing
○ The video data requires to get processed on time to extract
the full benefit of surveillance:
■ warning in case of emergency
■ something wrong is detected

INTRODUCTION
● Big Data architecture for streaming processing of
large amounts of images is proposed:
○ A real-time scalable system for automatic glasses
detection using video images.
● Contributions
○ A scalable low-latency architecture for image analysis
using Big Data technologies
○ Parallelization of a glasses detection strategy
○ Parametrized to detect other face attributes

PROPOSED SYSTEM AND ARCHITECTURE
● Glasses detection on face images
● Big data architecture for glasses detection on face
images

GLASSES DETECTION ON FACE IMAGES
● Image acquisition
● Face detection
● Preprocessing of detected face
● Get the feature sets
● Classify features
9
Image
acquisition
Face
detection
Pre-
processing
Build
features
Classifi-
cation

● Image acquisition
○ Read frame from input video
○ Convert it to grayscale

● Face detection
○ Viola & Jones algorithm is used:
■ robust (high detection rate:high TP and very low FP)
■ executed in real time

● Preprocessing of detected face in order to deal with:
○ pose, rotation, scale and inaccuracies of located face
○ noisepiece is placed at the same level as the eyes both in
height and width

● Get the feature sets: Local Binary Pattern (LBP)
○ LBP is a well-known technique in computer vision
■ LBP is a type of feature used for classification
○ LBP histogram (LBPH) is usually built for texture classification
○ LBPH into mxn regions is calculated to get spatial information
13
LBP
LBP

● Classify features
○ Support Vector Machine (SVM) is applied to classify the
feature sets
○ SVMs are a useful technique for data classification
■ have been proven useful in many pattern recognition
tasks i.e. face recognition
14
GLASSES
NO GLASSES
LBP histogram

BIG DATA ARCHITECTURE
● Big Data architecture is proposed
● Parallelize the different steps of the glasses
detection workflows.
○ Topology implemented with a streaming technology
Apache Storm
○ Storm is a distributed real-time computation system
released as open source by Twitter
● Parametrized to detect other face
attributes

Architecture in Storm: two elements
● Spouts read information from the source and emit
the data as K-V tuples
○ Reads from a message broker (RabbitMQ, Kafka) or
streaming API
● Bolts process information coming from the spouts
or other bolts.
● Storm defines topologies connecting bolts and
spouts to process in streaming
SPOUT
represented as
BOLT
represented as

VideoSpout:
● Split the video streaming into a sequence of images
(frames).
○ This Spout uses a shuffle grouping
■ Frames are randomly distributed across the next bolts
■ Each bolt is guaranteed to get an equal number of
frames

V&Bolt:
● Viola & Jones algorithm is applied for each frame
● The output of this algorithm is estimated positions
of detected faces as rectangles

NormalizationBolt:
● From each rectangle, it calculates the region
around the eyes
● Returns this region to next step

LBPHBolt:
● LBP operator is applied to the normalized region
● A spatially enhanced histogram is constructed
● These features are used in the next step

SVMBolt:
● Support Vector Machine (SVM) is applied on the
classification step.
● Glasses/no glasses classification will be finally
obtained

● Closed/open eyes classifier using the same architecture

● Smile classifier using another normalization bolt

● Gender classifier using another normalization bolt

● Type of glasses using another classification bolt
26
SPORT GLASSES
SAFETY GLASSES
SUNGLASSES
GOOGLE GLASSES

CONCLUSIONS
● Real-time Big Data architecture
○ Collect, maintain and analyze massive volumes of
images
○ It can be modified in order to detect other attributes:
■ smile, gender, age or face recognition classifiers

FUTURE WORK
● Deep Learning
○ Deep Learning algorithms in our pipeline detection
architecture will be proposed

Thanks for your attention
Alberto Fernández
alberto.fernandez@fundacionctic.org
A Real-Time Big Data Architecture
For Glasses Detection Using
Computer Vision Techniques
Alberto Fernández, Rubén Casado,
Rubén Usamentiaga

A real-time big data architecture for glasses detection using computer vision techniques

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