Abstract

Nowadays, the information provided by digital photographs is very complete and very relevant in different professional fields, such as scientific or forensic photography. Taking this into account, it is possible to determine the date when they were taken, as well as the type of device that they were taken with, and thus be able to locate the photograph in a specific context. This is not the case with analog photographs, which lack any information regarding the date they were taken. Extracting this information is a complicated task, so classifying each photograph according to the date it was taken is a laborious task for a human expert. Artificial intelligence techniques make it possible to determine the characteristics and classify the images automatically. Within the field of artificial intelligence, convolutional neural networks are one of the most widely used methods today. This article describes the application of convolutional neural networks to automatically classify photographs according to the year they were taken. To do this, only the photograph is used, without any additional information. The proposed method divides each photograph into several segments that are presented to the network so that it can estimate a year for each segment. Once all the segments of a photograph have been processed, a general year for the photograph is calculated from the values generated by the network for each of its segments. In this study, images taken between 1960 and 1999 were analyzed and classified using different architectures of a convolutional neural network. The computational results obtained indicate that 44% of the images were classified with an error of less than 5 years, 20.25% with a marginal error between 5 and 10 years, and 35.75% with a higher marginal error of more than 10 years. Due to the complexity of the problem, the results obtained are considered good since 64.25% of the photographs were classified with an error of less than 10 years. Another important result of the study carried out is that it was found that the color is a very important characteristic when classifying photographs by date. The results obtained show that the approach given in this study is an important starting point for this type of task and that it allows placing a photograph in a specific temporal context, thus facilitating the work of experts dedicated to scientific and forensic photography.

Highlights

  • Introduction iationsIn several areas, there are many photographs that are not dated, so it is very complicated to know when they were taken, regardless of the subject of the photograph

  • Taking into account the above, this study aims to design and implement a model based on Convolutional neural networks (CNNs) capability of determining the year in which a photograph was taken

  • The main objective of this work was the analysis of convolutional models applied to the recognition of photographs in order to estimate the year in which they were taken

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Summary

Introduction

Introduction iationsIn several areas, there are many photographs that are not dated, so it is very complicated to know when they were taken, regardless of the subject of the photograph (faces, people, cities, landscapes, buildings, etc.). The use of digital photography is widespread In these types of photographs, complementary information is stored in the EXIF (exchangeable image file) format data [1]. This information includes the date when the photograph was taken, its compression type, the file format and other characteristics that allow us to determine the date and place where a photograph was taken, and the device used to take it. In this case, it is easy to know the date when a photograph was taken

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