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.


Why Industries are adopting Face Recognition Technology as a game-changer

India is currently one of the fastest growing economies in the World. One of the hallmarks of success for the Indian Industry leaders have always been about how they cope up with a fast changing environment. In recent times, technology has been a key differentiator to determine who stays ahead in the race. To adapt to and proactively adopt newer technologies in the fast-changing technical landscape has become a key ingredient for success for the present-day Industry leaders.

Many of the new technologies and innovation have been horizontal in nature, meaning they have applications across multiple sectors and industries. Technologies like Internet, Bluetooth, IOT, 3D Printing etc have never been sector or a use-case specific, but brought revolutionary changes across various Industries that optimized existing businesses, addressed pain points that have not been addressed before and enabled industries to cater to various adjacent use-cases that remained unaddressed earlier.

Face recognition is a similar technology that has been horizontal in nature. Innovators in various industries have been trying to understand the fitment of this revolutionary image processing technology in their specific landscape. The questions they are pondering over are : Why Face Recognition Technology for them? How can it bring real impact?

Leaders tend to see new technology as magic wand to resolve multiple existing challenges. They ideate regarding the new doors that can open using new technology and use their domain expertise to understand what business problems the new technology can solve in their specific sector. Few of such examples would be like how Portable Face Biometric Technology has made a significant impact in eradicating subsidy leakage problem from various social welfare programs, as well as increasing the revenues of companies where on-field sales and marketing activities have a strong impact on the company’s balance sheet.

Leaders also identify what are the next best alternatives to the new Technology and then identify gaps in these available alternatives and explore to what extent new technology can plug those gaps. Verifying bank users and beneficiaries with their Facial imprints in existing databases like UIDAI (Aadhaar Cards), recognizing valuable customers when they walk into the Stores are few examples where existing alternatives were not able to address the gaps, which have been addressed by Portable Face Biometric Technology now.

Portable Face Biometric Technology is one of the most mature and reliable flavours of Face Recognition Technology and is available in the form of Smartphone based Face Recognition, Verification and Tracking system(SFRVTS). This Face recognition Technology identifies faces in images captured from any photo capturing devices like IP Camera, Tablet, WebCam, Mobile Phones etcIt handles effectively the cases of pose variations and lighting alterations during taking pictures, along with facial changes of an individual over a period of time.

We have attempted to compare this Smartphone based face recognition, verification and tracking solution (SFRVTS) on various parameters with competing technologies like Fingerprint scanner, USB based Fingerprint hardware, Iris scanner, first generation facial scanners, Log in & Log out based portals etc to understand comparatively the quantum of benefits it provides in various operational dynamics, various use cases along with features. Here is the comparison table:

For the Innovators & Decision Makers
Use case
For the problem solvers
While this is a small roundup of the Use-Cases and features of Face Recognition technology and similar technologies/options in action, we would love to hear from you if there have been any glaring omission.

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:

Tweet: How to begin your career in Computer Vision and Machine Learning Field? #AI #career #technology #CV #MLBooks:

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).

Which is the best off-the-shelf classifier?

If you are thinking of using some random classifier to solve the classification problem for your own data, then your best option would be to try Random Forest or a Support Vector Machines (SVM) with Gaussian Kernel. In a recent study these two algorithms have proven to be the most effective among nearly 200 other algorithms tested on more than 100 publicly available data sets.

In this blog post we are highlighting some important points quoted in the paper – “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?”,  which aid in choosing the right algorithm for our own machine learning problems.

Authors have evaluated 179 classifiers from 17 families on 121 datasets (total number of experiments is 241,637) and this  is definitely an exhaustive study of classifier performance with a significant contribution to our community. The dataset consisted of 10 to 130,064 patterns, from 3 to 262 inputs and from 2 to 100 classes. Here is the snippet of the classifiers in the ranked (ascending) order:

  • Random Forests with 8 variants.
  • SVM with 10 variants.
  • Neural networks with 21 variants.
  • Decision trees with 14 variants.
  • Bagging with 24 variants.
  • Boosting with 20 variants.
  • Other Methods with 10 variants.
  • Discriminant analysis with 20 variants.
  • Nearest neighbor methods with 5 variants.
  • Other ensembles with 11 variants.
  • Logistic and multinomial regression with 3 variants.
  • Multivariate adaptive regression splines with 2 variants
  • Generalized Linear Models with 5 variants.
  • Partial least squares and principal component regression with 6 variants.
  • Rule-based methods with 12 variants.
  • Bayesian approaches with 6 variants.
  • Stacking with  2 variants.

The average accuracy of the best performing 25 classifiers are shown in the below graph. Among them, the best performers are random forest  and SVM with Gaussian kernel with average accuracy of  82.0%(±16.3) and 81.8%(±16.2) respectively. Most of the experiments involved fine-tuning the parameters. The reported average accuracies are computed using 4-fold cross validation.

Average accuracies for the 25 best classifiers


Random Forest classifier has helped people win some Kaggle competitions. Here is what people have to say about usage of Random Forests:

“Since they have very few parameters to tune and can be used quite efficiently with default parameter settings (i.e. they are effectively non-parametric). This ease of use also makes it an ideal tool for people without a background in statistics, allowing lay people to produce fairly strong predictions free from many common mistakes, with only a small amount of research and programming.”




Final thoughts:

  •  Try random forests/Gaussian SVM as a baseline and later move onto new or advanced methods.
  • Always remember to standardize (scaling, transforms) your data, this has shown to significantly affect the performance.
  • Fine-tune the classifier parameters to get the best performance, you could do this by cross validation.
  • If possible try to extract better features (may need domain knowledge) from the data which could aid the classifier to meet better performance.


Drop a comment and let us know what classifiers you are using in your products and what has been the experience so far.

Facial Recognition For On-the-field and In-the-office Employees

facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. One of the ways to do this is by comparing selected facial features from the image and a facial database.

It is typically used in security systems and can be compared to other biometrics such as fingerprint or eye iris recognition systems.

There are numerous biometric security systems in the world. These biometric systems are well adopted and  have proven their utility in multiple scenarios and conditions. Although, no technology is cent percent right but delivering a trust worthy and reliable solution for various use cases has always been a great challenge accepted by the technologist , researchers and entrepreneurs across the world.  Facial recognition system is one of the unique way of biometric identity verification since ages. This system includes many variations like access control system, visitor management system, time attendance system and many more. These applications enhance the security level to many premises such as schools, offices, government buildings, educational institutes, and other organizations. It is not only beneficial for security purposes, but also for controlling human resources by ensuring their presence at right time and location.

The Visual Identification & Tracking Systems (MVITS) is a novel and creative way to track and monitor your on-field and in-office employees/staff. MVITS is a unified solution which ensures both the on field force employees presence at right time and location and simultaneously ensuring the check in  check out time of the in-the-office employees.

Currently the visual identification system is based on the patent pending technology of facial recognition which widely used to record several factors like on-field & in-office presence and absence,location details of the meetings, overtime and under time, authorized and unauthorized leaves, etc. of each employees and staff of the company. This system includes several steps which enables its tracking work. The first step involves the employees to enroll themselves by using their mobile/desktop/laptop devices which has built in web cam . Based on the concept of BYOD– Bring Your Own Device, it captures the facial features of individual employee and uploads to the cloud to build the database. This process of building the database is OTP- One Time Process called “enrolment”.  Next time when the employee/staff  use the application,  it automatically detects and recognises the employee to ensure his/her presence at the right time and location.

Still many small and medium companies are using the pen-paper and log book based attendance system. This is highly time-consuming and effort driven process. This traditional way of ensuring the productivity has always  been prone to problems like data manipulations, proxies, data loss, data mismanagement , lack of analytical information and many more.