Open-set face recognition across look-alike faces in real-world scenarios

CAPTCHA is one of the Turing tests used to classify human users and automated scripts. Existing CAPTCHAs, especially text-based CAPTCHAs, are used in many applications, however they pose challenges due to language dependency and high attack rates. In this paper, we propose a face recognition-based CAPTCHA as a potential solution. To solve the CAPTCHA, users must correctly find one pair of human face images, that belong to same subject, embedded in a complex background without selecting any nonface image or impostor pair. The proposed algorithm generates CAPTCHA that offer better human accuracy and lower attack rates compared to existing approaches.

http://www.iab-rubric.org/papers/faceCaptcha.pdf

Face recognition CAPTCHA

Face recognition has been one of the most interesting technology to study for many researchers. It allows a huge number of face images to be recognized in just a short amount of times, rather than recognizing each image individually through a normal human’s eyes. Its idea is generally based on the assumption that each individual has a unique identity that can be distinguished between one another. However, in the real world, there are individuals who have similar faces to each other. They are referred as "look-alike" faces. This research was conducted to recognize look alike faces. Each image is represented by the features it contains. The classification method used in this research was Support Vector Machine, with the implementation of kernel. Two types of kernel used in this research were the Radial Basis Function kernel and the polynomial kernel. Results showed that SVM with Radial Basis Function kernel obtained higher accuracies.

Face Recognition to Identify Look-Alike Faces using Support Vector Machine

Face recognition has found several applications ranging from cross border security, surveillance, access c ontrol, multimedia to forensics. Face recognition under variations due to pose, illumination, and expression has been extensively st udied in literature and several approaches have been proposed to address these covariates. Many applications of face recogn ition require matching face images with variations in age and disg uise such as matching a recent photo with your passport image or image on driver’s license. In literature, techniques have a lso been proposed to recognize face images with variations in age and disguise. These challenges can be grouped as existing covariates of face recognition. However, with ever increasing applica tions of face recognition there has emerged a need to understand ne w fascinating challenges in face recognition, emerging covariates of face recognition. Covariates such as forensic sketches, su rgically altered faces, low resolution faces, and look-alikes or twi ns are some of the challenges that have emerged as new covariates of face recognition. These covariates have important law enfo rcement applications; therefore, it has now become imperativefor current face recognition systems to be robust to these chall enges. This report focuses on three different aspects. First, it pr esents a review of different techniques proposed to address thexisting covariates, limitations of current techniques and future scope of advancements. Second, it presents how themerging covariates have evolved, what are the challenges, proposed techniques , and future research directions for each of these covariates. Fi nally, the report presents an evolutionary granular approach to address one of the emerging covariate, plastic surgery.

Covariates of face recognition

Ethnicity detection from facial images is very important information and it has numerous applications in the area of visual imagery. There are many texture features used for this method such as Local Binary Pattern, Harlick features etc. Most of the manuscripts are utilizing combination of other features with texture features and then classification algorithms. We have modified the texture features with the inclusion of harlick features of gray level co-occurrence matrix for ethnicity detection. These features are extracted from the segmented skin image. Also for texture features, effect of Bagging along with Random trees is highlighted to carry out efficient classification. We have performed evaluation of past texture features and our proposed texture feature method.

Modified Texture Features from Histogram and Gray Level Co-occurence Matrix of Facial Data for Ethnicity Detection

One of the major challenges of face recognition is to design a feature extractor and matcher that reduces the intraclass variations and increases the inter-class variations. The feature extraction algorithm has to be robust enough to extract similar features for a particular subject despite variations in quality, pose, illumination, expression, aging, and disguise. The problem is exacerbated when there are two individuals with lower inter-class variations, i.e., look-alikes. In such cases, the intra-class similarity is higher than the inter-class variation for these two individuals. This research explores the problem of look-alike faces and their effect on human performance and automatic face recognition algorithms. There is three fold contribution in this research: firstly, we analyze the human recognition capabilities for look-alike appearances. Secondly, we compare human recognition performance with ten existing face recognition algorithms, and finally, proposed an algorithm to improve the face verification accuracy. The analysis shows that neither humans nor automatic face recognition algorithms are efficient in recognizing look-alikes.

Face recognition for look-alikes: A preliminary study

One of the major challenges of face recognition is to design a feature extractor that reduces the intra-class variations and increases the inter-class variations. The feature extraction algorithm has to be robust enough to extract similar features for a particular class despite variations in quality, pose, illumination, expression, aging and disguise. The problem is exacerbated when there are two individuals with lower inter-class variations, i.e., look-alikes. In such cases, the intra-class similarity is higher than the interclass variation for these two individuals. This research explores the problem of look-alikes faces and their effect on human performance and automatic face recognition algorithms. There is two fold contribution in this research: firstly, we analyze human recognition capabilities for lookalike appearances and secondly, compare it with automatic face recognition algorithms. In our analysis, we observe that neither humans nor automatic face recognition algorithms are efficient for the challenge of look-alikes.

Can humans and automatic algorithms recognize look-alike faces?

Counterfeit currency varies from low quality color scanner/printer-based notes to high quality counterfeits whose production is sponsored by hostile states. Due to their harmful effect on the economy, detecting counterfeit currency notes is a task of national importance. However, automated approaches for counterfeit currency detection are effective only for low quality counterfeits; manual examination is required to detect high quality counterfeits. Furthermore, no automatic method exists for the more complex – and important – problem of identifying the source of counterfeit notes. This paper describes an efficient automatic framework for detecting counterfeit currency notes. Also, it presents a classification framework for linking genuine notes to their source printing presses. Experimental results demonstrate that the detection and classification frameworks have a high degree of accuracy. Moreover, the approach can be used to link high quality fake Indian currency notes to their unauthorized sources.

/pdf/detecting-counterfeit-currency-and-identifying-its-source-2tp8al0zrf.pdf

Detecting Counterfeit Currency and Identifying Its Source

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Papers

Face recognition for look-alikes: A preliminary study

Can humans and automatic algorithms recognize look-alike faces?

Detecting Counterfeit Currency and Identifying Its Source