AI-Based Face Detection and Recognition.

The key features of AI-based Face Detection and Recognition

AI-Based Face Detection and Recognition.

In Face detection AI does involve, at least the latest technologies do, and they do so in a stylish manner. Let’s take an example, we all have used a smartphone, now back in the days, these ‘smart-phones’ weren’t as much
smart as they are today, back then Artificial Intelligence wasn’t really much involved but after the machine-brain being introduced into these lifeless robots, many industries saw a disruptive change.

Some fact might you aware of that Face recognition existed way before iPhone X was released right? Face recognition is nothing new but the technology is so advanced and after we came to know about artificial
intelligence it created a revolutionized facial recognition system. Have you ever wondered how so many social sites such as Snapchat, Instagram, Facebook, etc filters work perfectly on your face?

How do they have the exact ability to accurately map the geometry of your face?

The advancement in technology which embedded artificial intelligence to create a smart system able to do those things. Face ID spontaneously adapts to changes in your appearance. From cosmetic to glasses, makeup, glowing face or even growing facial hair. It is designed in such a way which able to work indoors, outdoors and even in total darkness. Something very interesting!

Face Recognizer searches an actual collection of data of faces and compares them with the faces detected in the scene to find a match. Face Recognizer records face from the camera and detect “people of interest” providing real-time alerts upon detecting certain faces in the scene.

Key Features of Face Recognizer Detect and log faces.

  • Recognize faces.
  • Search for similar faces.
  • It is able to detect any matches with faces on the watch list database and provide alerts.
  • It creates a log of people in the scene which can be used for forensic investigations.
  • According to public standard data set the Facial recognition accuracy is up to 99.6%.
  • Real-time watchlist detection. Identifying individuals on the spot.
  • Going to safer cities by capturing an image, evaluating it and matching it.

Face Recognition can be recognized by anyone with an arrest warrant out on the loose. Recognition and detection are possible both in real-time and offline and recognition and enrollment are available from both video and still images. Recognition in video mode is achieved by analyzing multiple images per face and in the video, mode recognition is achieved in less than 1-2 seconds. Using a mixture of AI and Deep Learning, Face Recognizer has achieved certainty benchmarks similar to or better than industry leaders like Google and Facebook. It scores the following accuracy in the leading public test databases – LFW: 99.3%, YouTube Faces: 96.6%, MegaFace (with 1000 people/distracters): 95.6% process.

It is said that technology presented us with the digital interview. However, with the beginning of facial recognition in recruiting, the process becomes quite subjective and bias. It is now possible for recruiters to use face recognition algorithm to analyze face against huge datasets sourced from social sites or any other networking sites.

Hiring managers can look you up online and use the information they learn about you to make their decisions. In the past, all you had to do was send in your application and patiently await a callback. We should thank this technology, several other auxiliary factors come to play before you are considered for an opening thus making the process flawed to some extent.

In present face recognition, the conventional pipeline consists of four stages:

  1. Detection,
  2. Align,
  3. Represent,
  4. Classify.

Many networking sites often visit both the alignment step and the descriptions step by step employing explicit 3D face modelling in order to apply a piecewise affine transformation, and which further derive a face representation from a nine-layer deep neural network. It has a facial recognition research project called DeepFace.

DeepFace is now almost as accurate as of the human brain. DeepFace can
see at two photos, and irrespective of lighting or angle can say with 95.35% accuracy whether the photos contain the same faces or not. There are lots of advanced shape and colour based algorithms for face detection in special use-cases. One advanced API which is based on the accuracy and consistency of results is a Chinese company called Face++.

Nowadays, many people are showing their interest in this field and if you’re interested in industrial applications of face detection such as biometric logins, or advanced attendance or security applications.

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How data science is useful for banking professionals?

How Data Science is helping banking professionals for building effective strategies

How data science is useful for banking professionals?

Data science in the banking industry is causing a sweeping transformation as per the changing consumer behaviours and expectations, more strict regulatory guidelines and highly competitive environment bind a potential success in the area of banking.

In Standard Chartered, Data Scientists tend to convert raw data to useful insights into business decision-making. This is achieved by leveraging tools and techniques, such as Machine Learning, Qlikview for visualizations, Hadoop for handling Big Data, R & Python programming for building data models, etc. Banks have always been a custodian of customer data, but they lack the technological and analytical capability to derive value from the data.

Data science will be the linchpin of future personalised banking services, Bank of Ireland’s “Colin Kane”. Data science is already a core fixture at Bank of Ireland, where various teams of data scientists and data experts are involved in analytics functions such as risk analytics, anti-money laundering (AML) fraud, marketing and customer experience.

The analytics market in India is growing in leaps and bounds. “The analytics business in India has created progress in terms of adoption across industries, nonetheless it’s obscurity on the brink of its potential being a broach, applying analytics to banking and finance is crucial, profitable, and intensely appreciated, each for organizations and professionals. That Decades went when a typical bank client would walk into a bank and be greeted by a banker who knew his name, his personal backgrounds and the way best to serve his personal banking desires.

This can be quite a recent model wherever banks have non-inheritable and preserved the customer’s trust and served them for a protracted time. However, things modified. individuals usually are engaged in multiple assignments and travels to completely different geographical locations. If at some point he stays in the national capital, the immediate next day he might get to visit Paris on his business assignments. In such conditions, it’s difficult for a banker to trace his personal preferences and whereabouts to satisfy his desires. Massive knowledge offers insight into several complicated areas of individual’s life together with their manner, needs, and preferences of their customers so it’s simple for banks to personalise services to the requirements of every individual.

For a protracted time, the banks miserably didn’t utilize the data generated by their own business. the massive knowledge has become a game-changer in remodelling their business method and conducts to spot business opportunities and potential threats. Generally, banks and money establishments notice massive knowledge from sources like knowledge, transactions, helplines, emails, social media, external feeds, support, audio, video and a few alternative sources.

Data, as we know, has been the driving issue for several sectors and organizations. And one sector wherever it adds extreme worth is that the one that deals with capital – banking and finance. Finance features is a crucial role in boosting the worth and growth of a business and banking permits quality and wealth generation. As key business and economic functions, banking and finance overlap with analytics as they inherently cope with the information.

Applying Analytics to money information opens whole new avenues of insights and understanding of the business, the market, their performance, and their growth. The ongoing transformation is opening doors to several opportunities. Banks must ensure that they can cost-effectively acquire new customers while retaining existing ones. And to expand their reach and profitability, they must also tighten their focus on the expanding digital world. Analytics, big data and data science can unlock a world of new possibilities.

With the proper use of data science, banks can better understand prospect/customer relationships by exploring ever-changing transactional and interactional behaviours. New digital marketing technologies, such as Web sites, e-mail, mobile apps and social networks, are helping banks better target their customers and improve engagement.

Moreover, advanced segmentation strategies are helping them boost their marketing effectiveness by identifying niches based on consumer behaviour. Many banks are just beginning to consolidate and utilize the internal data elements at their disposal, such as debit and credit transactions, purchase histories, channel usage, communication preferences, loyalty behaviour, etc. And when it comes to big data, banks have collected large amounts of information from a variety of sources, such as transaction details and spending behaviours. The addition of newer sources, including Web server logs, Internet clickstreams, social media activity and mobile-phone call details, has opened the floodgates on the data sets that can be mined for meaning.

Introduction of massive knowledge in banking has destroyed several ground rules of business and remodelling the landscape of the money services business. With an enormous volume of information gushing from innumerable transactions, the banks try to seek out innovative business concepts and risk management solutions.

Every set of data gathered over an amount and tell a novel story and show the post for a particular future amount so a house will take advantage of this information to realize a competitive edge up the market. This massive analytics knowledge will improve the extrapolative power of risk models employed by banks and money establishments, analytics also can be employed in credit management to find fraud signals and same will be analyzed in real-time victimization AI. On a significant note, banking and finance business cannot understand knowledge analytics
in isolation.

Alongside distinguishing business opportunities, they must establish security threats, the prevalence of fraud and potential remedies. Further, they must arrange to connect analytical knowledge across division and structure silos. In several of the normal banking entities in India haven’t nonetheless begun their massive knowledge activities. The banks have a footing in capitalizing on those opportunities.

Therefore, massive knowledge science not solely brings new insights to the banks, however, it conjointly allows them to remain a step previous the sport with advanced technologies and analytical tools. As an early warning system, data science solutions can help banks quickly identify potentially fraudulent behaviour before the fraud becomes material. For example, individual cardholders are creatures of habit.

Cardholders have “favourites “ or recurrences over a wide variety of objects in their transaction streams. These objects might include favourite ATMs that are close to work or home or gas stations along a daily commute, as well as preferred grocery stores and online sites for shopping. An analytics technique that could be used to improve fraud management is to identify cardholder favourites, in order to distinguish between “in-pattern,” or normal, customer spending and “out-of-pattern” suspicious transaction activity.

This enables faster fraud detection at much lower false-positive rates (declines on legitimate transactions). Text analytics of unstructured data can help banks identify patterns of information that indicate the likelihood of fraud. Text mining of insurance claim descriptions (written and recorded) provided by bogus claimants uncovered some very interesting facts. It turns out that certain phraseologies (the use of “ed” rather than “ing” on the end of verbs, for instance), are extremely indicative of
fraudulent claims. This is due to the different ways in which people rely on stories they actually experienced vs. those they concocted; for instance “I
was walking” is indicative of someone recounting an actual experience whereas “I walked” often turns out to be indicative of someone describing
a fictitious event.

DBS Bank, Southeast Asia’s largest bank by market capitalisation, is driving a digital transformation process with data at its heart, with goals including the growth of its customer base in India from 1.8 million to 5 million by 2021. DBS will look into improving its data analytics set up to enhance areas like robotic process automation – 8 per cent of customer interactions are handled by bots – and other more advanced applications such as anti
money laundering: ït’s still early days but we will be enhancing analytics to drive a lot more productivity and more added-value services and processes to the bank.

For instance, machine learning can be used to understand customer sentiments.

All calls to the bank’s call centres are recorded. They can be converted to text and then machine learning algorithms can be used on the analytics platform to understand the sentiment. Problems can be flagged so that the bank can reach out to the customers.

Ultimately behavioural information and machine learning, in combination with biometrics could even enable ‘invisible authentication’, where a customer no longer needs to provide any supporting documents or use a physical device for transactions According to Gartner report, Yes Bank carried out the successful deployment of data analytics for enhancing its business impact, integrating data science in their core model showcasing the urge to create data science-driven decision making.

Techniques to gain insights into customer behaviour, services and predictive models using different types of customer transactional and behavioural data. Impacting the revenue growth with high margins, controlling risk management and enhancing customer experience. Axis, third-largest private-sector banks in India, recently launched a hackathon to solve a signature recognition model.

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Data science career insights that you must know

Top Career Insights to know in Data Science

Much has been written about ‘data is the new oil’ and ‘data science’ is one of the hottest emerging technology areas. Yet many of the aspirants who would like to get into this sector have many questions about the skill requirements, opportunities in the industry, job openings etc. In this article, we shall try to answer a few of the commonly asked questions and clear the ambiguity around the required skills and job roles.

● Almost 48% of analytics job openings are looking for a B.E./B.Tech graduate degree in the incumbent.
● 18% analytics job openings are looking for a postgraduate degree which is not MBA or M.Tech. This is a decrease from 26% a year ago.
● 8% of analytics jobs specifically require an MBA/PGDM degree.
● Just 13% of recruiters are looking specifically for graduates with no/ non-B. Tech degrees, up from 10% last year.
So here’s the question in everyone’s mind that then how do a fresher crack this industry?
I am interested in getting into data science as a career. What do I need to learn?

In the area of data science, broadly understanding of three areas are required:
1. Statistical /machine learning techniques & algorithms
2. Computing tools/languages
3. Business understanding

For the first two areas, skills need to be built through learning and training.
Business understanding is a function of exposure in the business and industry.
One can prepare by learning through examples, case studies, projects etc.

It will lead you to become an expert in data science. A data science expert is someone who can analyze huge amount of unstructured and structured data using advanced tools and machine learning algorithms to solve a real-world business problem.

Skills that are needed to get an entry-level job in this field.
1. Basic of statistics.
2. SQL
3. Knowledge of basic problem-solving.
4. Having a portfolio of projects.

In statistics, you need some basic knowledge like
1. Sampling.
2. P-value.
3. Hypothesis testing.
4. Experimental design.

Try to implement these concepts using python on small data sets, so that you can tell anyone that you are you have some practical knowledge. By doing this you will have an edge over other candidates who may just have a theoretical understanding of these concepts.

SQL will help you
1. To retrieve data from relational database
2. It stores data in your databases.
Many data analytics professional spend their time on writing SQL scripts.
You may get various job descriptions like data scientist, business analyst or big data this might be confusing for you you but do not get confused by different job titles by companies providing.
In reality, they go in the path of data analytics after B.Tech graduation in this
industry.

Python, R, MySQL or Excel are mandatory skills for this job. They are also looking for candidates who have a good knowledge of the algorithm and if you know the basic concepts in machine learning, then it would be like “icing on the cake”.

What are the job roles in data science?
Data Science Aspirants wonder about the kind of roles that are there in the industry for them. There are many roles and opportunities in organizations. It is pertinent to remember that data science or big data analytics can not be done with one person. It is a team that works on such projects and there are various functions
and roles that are needed. Let us decipher a few of such job roles and designations that you may come up with.

1. Data Scientist
A data scientist is like the master chef! He is expected to work with big sized and complex data to derive meaningful patterns/visualization for the business to benefit. He should know statistical and machine learning
algorithms, how to manage complex data sources as well as have a strong understanding of business. A tall order of requirements from a single person but a data scientist adds tremendous value to an organization’s data science and analytics journey.
2. Data Analyst
Data Analysts are curious people who have an analytical mind. They sift through tons of data to find out if there is a relationship by running statistical analyses or find out the root causes of an event happening, like, why the sales of a division in a geography is going down for a product line or developing a model for predicting fraudulent claims for a health insurance company.

3. Data Engineer
Data Engineers are the people who love to play with large scale databases and systems and typically come with a software engineering background. ln, a data science context they operationalize the analytics model developed by data scientist and data analyst team and, automate, deploy and make it run for clients/business.

4. Data Architect
In the context of a large organization with disparate sources of data, the role of data architect is very important. He creates the blueprint and map for data sources and data mart which is crucial for any data science or analytics
project.
5. Business Analyst
A business analyst, on the other hand, is more business savvy and looks at problems from the angle of business rather than the technicalities of data, algorithm, architecture, management etc. He is the one who will have a
better business/domain knowledge and uses data to support business processes. He may use different visualization and analytics tools/products for his findings and recommendations. Needless to say, all the above may start at entry-level and grow to senior levels and, also there could be other roles and nomenclatures that you
may come up with like data integrators, database administrators, data warehouse specialists, big data consultants etc. and there are always some overlaps in functions among these roles. NASSCOM has identified six areas of
specialization in the Big Data Analytics domain that is expected to drive growth in the sector: business analysts, solution architects, data integrators, data architects, data analysts and data scientists.

Where are the job opportunities?
Data Science job scenario in India is constantly evolving. Companies are looking to hire professionals who are looking to hire professionals who are well trained with new tech concepts such as analytics, big data, data science, artificial intelligence and machine learning among others.

● Around 62% of analytics requirements are looking for candidates with less than 5 years of experience.
● 17% of analytics jobs are for freshers.

Opportunities in data science are no more limited in only the technology or consulting companies.
It is finding greater acceptance in traditional businesses as the interest increases in leveraging data for decisions. The number of opportunities of data scientists and data engineers can be found in companies like Uber, Amazon, Ola, Google etc. as their business models are heavily data-dependent as well as in consulting companies like PWC, KPMG, Deloitte etc.

10 leading organizations with the most number of analytics openings this year are – JPMorgan, Accenture, Microsoft, Adobe, Flipkart, AIG, Ernst & Young, Wipro, Vodafone & Deloitte.
Then there are user companies in healthcare (hospitals, pharma etc.), retail, telecom, utility, BFSI (banking, financial services and insurance) etc. where there is a large number of opportunities as data analysts, business analysts, reporting specialists etc. The startup companies are also coming up with new product and services using analytics and artificial intelligence which are creating new job opportunities.

Top designations advertised are Analytics Manager, Business Analyst, Research Analyst, Data Analyst, SAS Analyst, Analytics Consultant & Statistical Analyst, Data Scientist.

l am a fresher. Where do I start?

Innomatics Technology Hub offers quality-driven Data Science courses in
Hyderabad, India. We at Innomatics provide very comprehensive courses at
Hyderabad. People from different part of cities come across here to get knowledge
in specific domain i.e Data Science. We Innomatics provide hands-on an experiment

with 100% of placement once you are done with your course.

Nowadays every fresher graduate is interested in analytics as an industry. Why is
everyone moving towards data analytics jobs? Why there are more interested in
this field?
Because analytics is one of the fastest-growing industries in the world today. The
demand so vastly surpasses the supply of data analysts that companies find it
difficult to hire trained professionals in analytics. Innomatics Technology helps
freshers as well as experienced candidate with highly qualified trainers to help you
land on a data science job with full placement support. Innomatics will also explain
analytics concepts in a simple way to you, while also demonstrate how these
concepts are implemented in a series of projects, which will make your learning
process effective, easier and hassle-free.

Can IT professionals be reskilled for these new-age job opportunities?
With the rising demand for data-driven technologies across the globe, IT companies
are reskilling their large pool of IT professionals in these new-age technologies like
predictive analytics, machine learning, data mining, deep learning, cognitive
analytics, artificial intelligence (A), IOT based analytics etc. Best SAS, SPSS, Cognos,
R, Python, IBM Watson, Hadoop, Spark etc., – proprietary or open
Apart from training aspirants and practitioners incorporates in business
analytics, big data, data science etc., Innomatics also offers new courses like
cognitive analytics using IBM Watson, machine learning with Python etc. Many of
our students have successfully moved on from being an aspirant to successful
practitioners in the industry.
Startups and young organizations are also using these new technologies to come up
with disruptive solutions and business models. This is also adding to the
opportunities for existing professionals for lateral shifts.

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