**2. Issues and challenges**

Object detection is a difficult task mainly because of possible changes in the appearance of the object due to different consequences. The design of the potential method must consider the possible difficulties:

**349**

*Biometric Systems and Their Applications DOI: http://dx.doi.org/10.5772/intechopen.84845*

single point of view.

• Intra-class variations: An object type can have a large number of variations (**Figure 2** illustrates different types of chairs). This can pose a problem by using specific features, which do not cover all possible object variations.

• Luminance conditions: Variations in luminance conditions change the appear-

• Point of view: The majority of objects in the images are in three dimensions. The images are only two dimensions, which means that we can only see a particular view of the given object. The same object may differ from other points of view, which we must also be aware of. Different views of the same object cause the invisibility of different features. Not all features are visible from a

• Scale: The size of the object may differ, and there is a desire to be able to detect

• Location: It is much easier if you know where the desired object is. The situation differs if you have a prior knowledge of the location of the object and no information about it or if you know that the image contains only one object,

• Orientation: Humans do not have obvious problems recognizing the same object with a different orientation, but many algorithms do. Invariance to this

• Occlusion and truncation: Occlusion from one object to another often causes a lot of inconvenience. Sometimes even humans do not see enough features of the object to recognize correctly. Truncation is the same problem when you do

not see certain parts of the object, because they are out of the picture.

• Footprint: The background is almost nonexistent; an image contains only one chair. This situation is not typical in a real world. When you take the image of an object, there are almost always many other objects in the background, in which the recognition algorithm is usually not interested. The scene is often very complex, and it is difficult to recognize the object/objects desired among other objects.

• Out of context: Context is often used to increase the likelihood of certain categories of common occurrences. For example, cars and roads are often associated, but we cannot rely too much on context because sometimes it can be misleading.

• Multiple instances: The image often contains several objects of the same category. Some algorithms can identify regions of different categories in the image, but they cannot identify individual instances of the same object category.

• Pose: One of the biggest challenges is the invariant detection of the pose. Many objects change their appearance by changing their shape. For example, it is

• Instance level recognition vs. object class recognition. It is necessary to realize the difference between these problems. It is obvious that different methods are needed to recognize the human face in general and the person who uses the face.

desirable to detect a person in any posture of their body [1].

ance of the object, mainly in color and reflection.

what the object gives to any size or scale.

located in the center of the image.

possibility is often crucial.

### *Biometric Systems and Their Applications DOI: http://dx.doi.org/10.5772/intechopen.84845*

*Visual Impairment and Blindness - What We Know and What We Have to Know*

The visual perception of the surrounding world is among the most important. It could be used to recognize pedestrians on the street, cars, animals, or even unspecified

Improving and developing object recognition algorithms will help improve not only artificial intelligent systems but many other useful applications in today's world. Other examples of application of this system can be extended to the tourist industry where applications of augmented reality (**Figure 1**) are becoming more and more popular especially after the widespread use of smartphones. In addition, the field of video surveillance is also a possible extension of object detection algorithms because of the need for quick and timely detection of different video

Indeed, scene comprehension includes many separable tasks ranging from object recognition to the categorization of scenes and events. Object detection is a

• Image classification: The search for images in the majority of search engines is a

• Object detection: The location of the object on the request is one of the desired

• Segmentation of objects: Which pixels belong to which objects? It is more

Object detection is a difficult task mainly because of possible changes in the appearance of the object due to different consequences. The design of the potential

objects on the road, which could pose a potential threat to human life.

complex discipline that can be divided into three main directions:

precisely compared to object detection for obvious reasons.

typical case of image classification algorithms.

information in many of the systems mentioned.

method must consider the possible difficulties:

scenes captured by cameras.

**Figure 1.** *Augmented reality.*

**2. Issues and challenges**

**348**


**Figure 2.** *Image search results.*

After analyzing the potential problems associated with recognition tasks, we believe that the direction to follow in imitating the human visual perception system is natural. The first moment of human comprehension of the image is a very general activity that analyzes the basic categories (buildings, men, cars, etc.). After getting the big picture, his attention focuses on the things that interest him. While focusing, humans observe objects of interest to enrich more details and see and recognize more features. A feature is a general term for describing a particular part of the object in order to enrich its appearance. A human has special predispositions on several objects (e.g., faces) and on situations (mainly of the danger and movement type) on which he is more sensitive to recognize. The typical situation is when you see someone away from you and you can recognize that it is a person. As you get closer, by focusing on this person, you are enriching and recognizing more and more elements that make it possible to distinguish whether he is a known person and to detect his name. Humans can do instance-level recognition as in the case presented, but they must first distinguish the object category to optimize the subsequent search.

### **3. Biometric systems overview**

#### **3.1 History of biometrics**

Biometrics has been a concern for centuries. Proving one's identity reliably was done using several techniques. From prehistory man knew the uniqueness of fingerprints, which meant that signatures by fingerprints were sufficient to prove the identity of an individual. Indeed, two centuries before Christ, the Emperor Ts-In-She authenticated certain sealed with the fingerprint.

At the beginning of the nineteenth century, in France, Alphonse Bertillon launched the first steps of the scientific police. He proposed the first method of biometrics that can be described as a scientific approach: bertillonage allowed the identification of criminals through several physiological measures.

At the beginning of the twentieth century, biometry was rediscovered by William James Herschel, an English officer who had the idea of having his subcontractors sign their fingerprints to find them easily in case of unhonored contracts. As a result, police departments have begun using fingerprints as a unique and reliable feature to identify an individual.

Biometrics is constantly growing especially in the field of secure identity documents such as the national identity card, passport, or driving license. This technology is running on new platforms, including chip cards based on the microprocessor.

**351**

*Biometric Systems and Their Applications DOI: http://dx.doi.org/10.5772/intechopen.84845*

**3.2 Biometric market development**

annual increase of 40% in 5 years.

identity with a person.

following elements:

**3.3 Biometric systems**

of biometric solutions in several social and legal fields.

The biometric market has undergone a great development thanks to the great number of advancement and innovation that this field has experienced in recent decades. This development is increasing as a result of the security concerns of several countries, which has pushed investment in this area and the widespread use

As shown by the statistics in **Figure 3** between 2007 and 2015, there has been a considerable increase in the share of the private sector market due to the growing need for biometric solutions in this sector especially for smartphone and camera manufacturers. According to ABI Research [2], the global biometric market will break the \$30 billion mark by 2021, 118% higher than the 2015 market. In this context, consumer electronics, and smartphones in particular, are boosting the biometric sector: it is expected to sell two billion onboard fingerprint sensors in 2021, for an average

A biometric system is a system that allows the recognition of a certain characteristic of an individual using mathematical algorithms and biometric data. There are several uses of biometric systems. There are systems that require enrollment upstream of users. Other identification systems do not require this phase.

• Enrollment mode is a learning phase that aims to collect biometric information about who to identify. Several data acquisition campaigns can be carried out to ensure a certain robustness of the recognition system to temporal variations of the data. During this phase, the biometric characteristics of individuals are captured by a biometric sensor, and then represented in digital form (signatures), and finally stored in the database. The processing related to the enroll-

ment has no time constraint, since it is performed "off-line."

identification number, a user name, or a smart card.

• The verification or authentication mode is a "one-to-one" comparison, in

• The identification mode is a "one-to-N" comparison, in which the system

which the system validates the identity of a person by comparing the biometric data entered with the biometric template of that person stored in the system's database. In such a mode, the system must then answer the question related to the identity of the user. Currently the verification is carried out via a personal

recognizes an individual by matching it with one of the models in the database. The person may not be in the database. This mode consists of associating an

**Figure 4** presents the architecture of a biometric system, which consists of the

• The capture module that represents the entry point of the biometric system and consists in acquiring the biometric data in order to extract a digital repre-

• The module of signal processing makes it possible to optimize the processing time and the digital representation acquired in the enrollment phase in order to optimize the processing time of the verification phase and the identification.

sentation. This representation is used later in the following phases.
