Person identification based on the TensorFlow machine learning library

Područje umjetne inteligencije u današnje se vrijeme koristi gotovo u svakoj grani tehnologije kao i u sve većem broju strojeva i predmeta kojima svaki dan rukujemo. Neki su se dijelovi umjetne inteligenciju razvili do stupnja gdje je čovjek postao gotovo ovisan o rezultatima koje takvi, pametni sustavi daju. Primjena takvih sustava uvelike je promijenila način na koji ljudi pristupaju određenim tehničkim problemima. Njihova je glavna prednost, mogućnost samostalnog i kontinuiranog učenja koje konstantno unaprjeđuje preciznost i točnost takvih sustava, čime se oponaša djelovanje ljudskog mozga koji na isti način funkcionira u realnom okruženju. Jedna vrsta takvih pametnih sustava koja je postala veoma popularna u zadnjih desetak ili više godina, svakako je sposobnost računala da prepozna različita ljudska lica. Iako je taj proces u ljudskom mozgu gotovo automatiziran te ne predstavlja nikakav poseban napor, za računalo to je ozbiljan izazov kojega nije bilo lako riješiti. U ovome će se radu kreirati upravo jedan program čiji će zadatak biti prepoznavanje ljudskih lica u realnom vremenu. Da bi se to moglo ostvariti, potrebno je koristiti dva odvojena sustava, jedan zadužen za detekciju lica na slici, a drugi zadužen za prepoznavanje osobe čije se lice nalazi na slici. Korištenje oba sustava temelji se na tzv. prenesenom učenju, gdje se već kreirani sustavi prilagođavaju i modificiraju za neke specifične potrebe, u ovom slučaju za izradu sustava za prepoznavanje lica.Today, artificial intelligence is being used in almost every aspect of technology, as well as in growing number of machines and gadgets that we use on daily basis. Some areas of artificial intelligence are so advanced, that people almost depend on the results which such smart systems give on their output. Application of those systems has greatly changed the way we approach different technical problems. Main advantage of modern, smart systems is the capability to continuously learn by themselves, which results in much higher accuracy and precision. This learning is based on imitating human brain that works the same way in real environment. One example of these smart systems is definitely computer ability to recognize human faces, which has become very popular in the last 10 or so years. Although, the process of recognizing faces is almost automated in human brain, it has been a real challenge to implement such thing in computers and machines. In this paper, one such facial recognition system will be created. The main task of that system will be recognizing faces in real time using web camera. To accomplish that, it is necessary to use two separate subsystems, one for the face detection and the other for face recognition once face has been detected. Both subsystems are based on transfer learning, which means that they are already created and only need to be slightly modified for this special case of recognizing faces in real time