Identification using face regions: Application and assessment in forensic scenarios

Tomé, Pedro; Fiérrez, Julián; Vera-Rodríguez, Rubén; Ramos, Daniel

doi:10.1016/j.forsciint.2013.08.020

Cited by 49 publications

(53 citation statements)

References 24 publications

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“…This step eliminates variations in translation, scale and rotation in horizontal plane. After this normalization, facial regions are extracted and pre-processed, as detailed in [7].…”

Section: Methods: Soft Biometric Featuresmentioning

confidence: 99%

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Facial soft biometric features for forensic face recognition

Tomé

Vera-Rodríguez

Fiérrez

et al. 2015

Forensic Science International

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El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis paper proposes a functional feature-based approach useful for real forensic caseworks, based on the shape, orientation and size of facial traits, which can be considered as a soft biometric approach. The motivation of this work is to provide a set of facial features, which can be understood by non-experts such as judges and support the work of forensic examiners who, in practice, carry out a thorough manual comparison of face images paying special attention to the similarities and differences in shape and size of various facial traits. This new approach constitutes a tool that automatically converts a set of facial landmarks to a set of features (shape and size) corresponding to facial regions of forensic value. These features are furthermore evaluated in a population to generate statistics to support forensic examiners. The proposed features can also be used as additional information that can improve the performance of traditional face recognition systems. These features follow the forensic methodology and are obtained in a continuous and discrete manner from raw images. A statistical analysis is also carried out to study the stability, discrimination power and correlation of the proposed facial features on two realistic databases: MORPH and ATVS Forensic DB. Finally, the performance of both continuous and discrete features is analysed using different similarity measures. Experimental results show high discrimination power and good recognition performance, especially for continuous features. A final fusion of the best systems configurations achieves rank 10 match results of 100% for ATVS database and 75% for MORPH database demonstrating the benefits of using this information in practice.

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“…This step eliminates variations in translation, scale and rotation in horizontal plane. After this normalization, facial regions are extracted and pre-processed, as detailed in [7].…”

Section: Methods: Soft Biometric Featuresmentioning

confidence: 99%

“…On the other hand, holistic approaches attempt to identify faces using global representations, i.e., descriptions based on the entire image rather than on local features of the face. Also, there are hybrid methods that detect landmark points and then apply techniques used by holistic methods [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Facial soft biometric features for forensic face recognition

Tomé

Vera-Rodríguez

Fiérrez

et al. 2015

Forensic Science International

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“…The proposed facial regions extraction framework is described in detail in [4] and extended in [3]. In this framework, two kinds of regions extraction are defined: i) based on human facial proportions, and ii) based on facial landmarks.…”

Section: Facial Regions Extraction and Color Methodologymentioning

confidence: 99%

“…Then, similarity scores are computed in this PCA vector space (dimension 200, retaining an average of 98% of the energy of the original eigen-region space) using a Support Vector Machine (SVM) classifier with a linear kernel. The experimental protocol followed is described in more detail in [3].…”

Section: Experimental Protocolmentioning

confidence: 99%

“…There are some previous works where grayscale facial region-based recognition is studied [1][2][3] but non of them focus their attention in the color regions normally considered by forensic experts. In this work, we have extracted facial components (called from now on facial regions) following forensic protocols from law enforcement laboratories, allowing us to study individually the different facial regions normally considered in current practice of forensic examiners.…”

Section: Introductionmentioning

confidence: 99%

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Fusion of Facial Regions Using Color Information in a Forensic Scenario

Tomé

Vera-Rodríguez

Fiérrez

et al. 2013

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: Abstract. This paper reports an analysis of the benefits of using color information on a region-based face recognition system. Three different color spaces are analysed (RGB, Y C b Cr, lαβ) in a very challenging scenario matching good quality mugshot images against video surveillance images. This scenario is of special interest for forensics, where examiners carry out a comparison of two face images using the global information of the faces, but paying special attention to each individual facial region (eyes, nose, mouth, etc.). This work analyses the discriminative power of 15 facial regions comparing both the grayscale and color information. Results show a significant improvement of performance when fusing several regions of the face compared to just using the whole face image. A further improvement of performance is achieved when color information is considered.

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LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

Medapati¹,

Murthy²,

Sridhar

2019

Trans Emerging Tel Tech

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Digital transformation played a vital role in smart cities because of its ability to process the different data to providing sustainability, connectivity, and mobility to data effectively. According to the diver operational productivity, preserving public safety government law enforcement integrated that face recognition is more important in smart cities. Traditional face recognition system fails to predict the exact facial features that leads to reduce the facial recognition accuracy. The false facial point detection process maximizes the computation complexity. Therefore, in this work, effective and artificial intelligence internet of things-based facial expression detection system is implemented to predict and match the face from the database. Initially, the facial images are captured from the internet of things sensor device, which is processed by applying the Perona-Malik diffusion algorithm. Then, face location is cropped from the image, geometric face shape model is created for predicting the exact face from the template face image. From the face shape, different facial features are extracted from various region using Fisher linear discriminant analysis. The derived features are trained with the help of convolution network and the face recognition process is done by using the adaboost large memory storage and retrieval neural network. The network successfully recognizes the face from the template, which is used to eliminate the safety related risk in smart cities.

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Identification using face regions: Application and assessment in forensic scenarios

Cited by 49 publications

References 24 publications

Facial soft biometric features for forensic face recognition

Facial soft biometric features for forensic face recognition

Fusion of Facial Regions Using Color Information in a Forensic Scenario

LAMSTAR: For IoT‐based face recognition system to manage the safety factor in smart cities

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