Skill Sector innovation​s​ that promises to be a game changer​

When a biological feature of a user like Fingerprint, Face, Iris, etc is used to identify a user, it is known as biometric verification of the user. One of the important programs from the Indian Government where biometric verification is mandatory, is the Skill India program, where the Indian. Government provides monetary reward to every young person who successfully completes an approved skill training program with an affiliated training provider.


The objectives of a biometric verification and attendance solution is to eradicate the following challenges during the process of skill training:

  • Unauthorized beneficiaries: Unauthorized beneficiaries show-up during the training session against a registered Aadhaar ID which results in identity fraud
  • Ghost Beneficiaries:Mismatch in the number of beneficiaries actually present during the training session and numbers shown on attendance reports
  • Non availability of effective portable biometric devices:The existing finger-print solution requires a dedicated scanner and needs to be installed at a given location with provisions for power and electrical wiring.
  • Subsidy leakage:Padding up the number of trained beneficiaries submitted to government bodies not only defeats the overall purpose of skill training, but also leads to financial loss and corruption.
  • High drop-out rate of beneficiaries:Absence of centralized attendance tracking system does not allow the management to have a close check on the actual cause of the high dropout rate of beneficiaries from the classroom sessions.
  • Highinitial price and maintenance cost of existing Finger print hardware: Training sessions are conducted across the country in rural and semi-rural locations. Initial set up of existing fingerprint based hardware devices includes wiring, hardware install and internet setup. Considering the remote locations of most of the training centers, the setup becomes a tedious effort. This creates a lot of maintenance hassles later and long turnaround times, mostly due to time consumed in reaching to these far flung centers to deliver the support.

Aindra Labs has come up with biometric verification and attendance solutions that work even in an infrastructure deficient country like India. Our flagship products, based on our patent-pending Face-recognition engine, are called SmartVerify© and SmartAttendance© and work as user-friendly smartphone application for bio-metric verification of an user’s identity. SmartVerify© and SmartAttendance© work in tandem to help an organization, that is spread out across various branches in distant locations, to capture attendance for both in-the-office and on-the-field beneficiaries. The attendance information is geo-tagged with location and timestamp information, so that the organization can track the beneficiaries in a humanly auditable, smart fashion. SmartVerify© can verify the user’s identity from the facial photo captured in user’s Aadhaar card or a Passport size frontal face photo of the user. SmartAttendance© can produce central reports of periodic attendance for different frequencies like daily, weekly, monthly, etc. at the click of a button, along with other relevant information like location, duration, time-stamp, etc. while providing attendance, helping the management to track and manage the training beneficiaries closely.

A smartphone is usually the device of choice due to the ubiquitous-ness and low cost of the device. SmartVerify© and  SmartAttendance©  utilize user’s existing Smartphone. The face biometric solution from Aindra Labs is hardware-agnostic and also works with laptops, IP camera and even desktops with a webcam – essentially with any smart device that has a camera to take pictures. It is an innovation that promises to be a game-changer in the Skill India program of Indian Government and many other verticals where biometric verification of identity with location and time-stamp information of a mobile workforce is critical.

Based out of Bangalore, Aindra Labs creates Computer Vision and Machine Learning based products that address the problems of a huge magnitude in poor countries.

Our vision is to address problems of a huge magnitude with the aid of deep technology. We have been recognized as one of the top 10 Artificial Intelligence based start-ups in India.


How to begin your career in Computer Vision and Machine Learning Field?

This post is for Computer vision enthusiasts. We are highlighting the focal point of resources and computer vision tutorials for all CV aficionados and get themselves started in this emerging field. As a passionate beginner in the field of computer vision, we hope you find this useful.
Some prior knowledge about linear algebra, calculus, probability and statistics would definitely be a plus but its not always required. The most important thing is to get started and you can learn other essential things on the fly.
  • Computer Vision – Mubarak Shah (UCF) : All the materials related to the course are available online and what is more interesting is that even the video lectures are available.
  • Computer Vision – Subhransu Maji (UMass Amherst) : Provides access to all the lecture materials and assignments but there are no video lectures.
  • Visual Recognition – Kristen Grauman (UT Austin) : This Provides links to some of the interesting and fundamental papers in computer vision.
  • Language and Vision – Tamara Berg (UNC Chapel Hill) : This course is basically aimed towards exploring topics straddling the boundary between Natural Language Processing and Computer Vision.
  • Convolutional Neural Networks for Visual Recognition – Fei-Fei Li and Andrej Karpathy (Stanford University) : This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for computer vision tasks with a main focus on image classification.
Some additional resources:

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Computer Vision
In addition to this, it is very useful to be aware of most of the basic image processing techniques presented in this book Digital Image Processing – Gonzalez 2007
OpenCV Programming
Software Packages:
You can find an exhaustive list of links presenting code which implements some of the standard vision algorithms at
Major Conferences: 
Below are some of the major conferences listed in their ranking order.
Most of the papers published in the above mentioned conferences can be accessed at
Nice way to keep track of the conferences deadline is via
Now that you have acquired some knowledge of computer vision and Deep Learning (from the previous post), please feel free to compete in Kaggle competitions (best way to put your learning into practice).
If you would like to have any guidance/support in CV domain or have any additional resource information, we would love to hear it without judging you. You may drop your comments below.

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Deep Learning Online Courses, Reading Materials and Software Packages

Why do you want to learn Deep Learning?

Deep learning has been termed as one of the leading scientific breakthroughs in recent years, you can read an interesting article: MIT Technology Review. It has resulted in state of the art performance in a variety of areas, including computer vision, natural language processing, reinforcement learning, and speech recognition. It has attracted significant industrial investment with large groups at Google ( hired Geoff Hinton), Facebook ( hired Yann LeCun to head Facebook AI Lab), Baidu (hired Andrew Ng), IBM, Microsoft etc working on applications of deep learning. Most interestingly, we at Aindra are also making use of this cutting-edge technology to develop our products.


Do you want to learn about Deep Learning?

The best way is to take an online course. There are many online courses on Machine Learning but very few on Deep Learning. Here’s a list:

Deep Learning and Neural Networks: by Kevin Duh. It consists of only four lectures but provides an excellent foundational understanding at a level sufficient for anyone to start reading research papers in this exciting and growing area.

Neural Networks for Machine Learning:  You could listen to the lectures here from one of the legend Geoffrey Hinton from the University of Toronto. This course emphasizes both on basic algorithms and the practical tricks needed to get them to work well in applications like speech and object recognition, image segmentation, modeling language and human motion, etc.

Material for the Deep Learning course: Here is all the course related materials by the legend Yann Lecun, NYU. It also has pointers to some of the tutorials conducted at various conferences.

Convolutional Neural Networks for Visual Recognition: at Stanford University. Though, video lectures are not available online but you can still get access to an excellent course notes and assignments.

Deep Learning seminar course: by Lorenzo Torresani at Dartmouth University. This might not be an extensive list of papers related to deep learning but definitely mentions some of the important deep learning papers and its well categorised.

Neural networks class – Université de Sherbrooke: by Hugo Larochelle Provides an in-depth lecture videos along with the slides on Deep Learning topics starting from the very basics of Neural Network.


Apart from these online courses you could also find some interesting tutorial talks at various conferences/workshop, here is a partial list:

Deep Learning for Natural Language Processing: This was a tutorial at NAACL HLT 2013 presented by Richard Socher, Yoshua Bengio, and Christopher Manning.

Deep Learning of Representations-Google Techtalk: Hear it from Yoshua Bengio, University of Montreal, who has made significant contributions in the deep learning field.

Deep Learning methods for vision: This was one of the CVPR 2012 tutorial.

Software packages which support Deep Learning include

  • Caffe: Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license.
  • Torch7: an extension of the LuaJIT language which includes an object-oriented package for deep learning and computer vision. It is easy to use and efficient, built using LuaJIT scripting language, and an underlying C/CUDA implementation.
  • Theano + Pylearn2: Pylearn2 is a python wrapper that lets you use Theano’s symbolic differentiation and other capabilities with minimal overhead.
  • cuda-convnet: High-performance C++/CUDA implementation of convolutional neural networks, based on Yann LeCun work written by Alex Krizhevsky.


For any further materials related to Deep Learning you can find them at

Please leave your comments or suggestions to include more sources related to deep learning (that we might have missed in this article).