Visual animal biometrics: survey

Visual animal biometrics is an emerging research discipline in computer vision, pattern recognition and cognitive science. It is a promising research field that encourages new development of quantified algorithms and methodologies for representing, detection of visible features, phenotypic appearances of species, individuals and recognition of morphological and animal biometric characteristics. Furthermore, it also assists the study of animal trajectory and behaviours analysis of species. Currently, real-world applications of visual animal biometric systems are gaining more proliferation due to a variety of applications and use, enhancement of quantity and quality of the collection of extensive ecological data and processing. However, to advance visual animal biometrics will require integration of methodologies among the scientific disciplines involved. Such valuable efforts will be worthwhile due to the enormous perspective of this approach rests with the formal abstraction of phenomics, to build well-developed interfaces between different organisational levels of life. This study provides a comprehensive survey of visual animal biometric systems and recognition approaches for various species and individual animal based on their morphological image pattern and biometric characteristics. This comprehensive review paper encourages the multidisciplinary researchers, scientists, biologists and different research communities to design the better platforms for the development of efficient algorithms and learning models to solve the massive data processing, classification and identification of different species related problems.

[1]  Adel M. Alimi,et al.  A Preliminary Investigation on Horses Recognition Using Facial Texture Features , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[2]  Zaven Arzoumanian,et al.  An astronomical pattern-matching algorithm for computer-aided identification of whale sharks Rhincodon typus , 2005 .

[3]  T.,et al.  Effective insect recognition using a stacked autoencoder with maximum correntropy criterion , 2019 .

[4]  Sanjay Kumar Singh,et al.  Face recognition for cattle , 2015, 2015 Third International Conference on Image Information Processing (ICIIP).

[5]  Cheng Cai,et al.  Cattle face recognition using local binary pattern descriptor , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[6]  W. E. Petersen,et al.  The Identification of the Bovine by Means of Nose-Prints , 1922 .

[7]  Ulrich Stern,et al.  Analyzing animal behavior via classifying each video frame using convolutional neural networks , 2015, Scientific Reports.

[8]  Pietro Perona,et al.  Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Alexandra Branzan Albu,et al.  Computer Vision-Based Identification of Individual Turtles Using Characteristic Patterns of Their Plastrons , 2014, 2014 Canadian Conference on Computer and Robot Vision.

[10]  Sanjay Kumar Singh,et al.  Automatic identification of cattle using muzzle point pattern: a hybrid feature extraction and classification paradigm , 2017, Multimedia Tools and Applications.

[11]  Juan Chen,et al.  Panda facial region detection based on topology modelling , 2012, 2012 5th International Congress on Image and Signal Processing.

[12]  D. Scott.  Identification of individual Bewick’s swans by bill patterns , 1978 .

[13]  Ella F Cole,et al.  Studying the evolutionary ecology of cognition in the wild: a review of practical and conceptual challenges , 2016, Biological reviews of the Cambridge Philosophical Society.

[14]  Chelsea Finn,et al.  Vision-Based Biometrics for Conservation , 2013, MCPR.

[15]  Sanjay Kumar Singh,et al.  Hybrid BFO and PSO Swarm Intelligence Approach for Biometric Feature Optimization , 2016, Int. J. Swarm Intell. Res..

[16]  Mahmoud Hassaballah,et al.  Face recognition: challenges, achievements and future directions , 2015, IET Comput. Vis..

[17]  S. Pierce,et al.  Seeing Spots: Photo-identification as a Regional Tool for Whale Shark Identification , 2010 .

[18]  Tijs Neutens,et al.  Extracting spatio‐temporal patterns in animal trajectories: an ecological application of sequence analysis methods , 2016 .

[19]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[20]  Elizabeth A. Masden,et al.  Contrasting effects of GPS device and harness attachment on adult survival of Lesser Black‐backed Gulls Larus fuscus and Great Skuas Stercorarius skua , 2016 .

[21]  Lyndon O Brooks,et al.  Abundance of humpback whales in Oceania using photo-identification and microsatellite genotyping , 2012 .

[22]  L. Collins,et al.  Non-intrusive tracking of commercial broiler chickens in situ at different stocking densities , 2008 .

[23]  Ronald Poppe,et al.  A survey on vision-based human action recognition , 2010, Image Vis. Comput..

[24]  Hans-Peter Seidel,et al.  Outdoor human motion capture using inverse kinematics and von mises-fisher sampling , 2011, 2011 International Conference on Computer Vision.

[25]  James D. Crall,et al.  BEEtag : a low-cost, image-based tracking system for the study of animal behavior and locomotion , 2015 .

[26]  S. Mizroch,et al.  A Test of Computer-assisted Matching Using the North Pacific Humpback Whale, Megaptera novaeangliae, Tail Flukes Photograph Collection , 2003 .

[27]  S. Pierce,et al.  The use and abuse of photographic identification in sharks and rays. , 2012, Journal of fish biology.

[28]  Zoltan Szantoi,et al.  Small Unmanned Aircraft Systems for Low-Altitude Aerial Surveys , 2010 .

[29]  Michael L. Domeier,et al.  Annual re-sightings of photographically identified white sharks (Carcharodon carcharias) at an eastern Pacific aggregation site (Guadalupe Island, Mexico) , 2007 .

[30]  Aboul Ella Hassanien,et al.  Cattle Identification Based on Muzzle Images Using Gabor Features and SVM Classifier , 2014, AMLTA.

[31]  C. Sandbrook.  The social implications of using drones for biodiversity conservation , 2015, Ambio.

[32]  Andre Possani Espinosa,et al.  Scorpions: Classification of poisonous species using shape features , 2016, 2016 International Conference on Electronics, Communications and Computers (CONIELECOMP).

[33]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[34]  Joachim Denzler,et al.  Chimpanzee Faces in the Wild: Log-Euclidean CNNs for Predicting Identities and Attributes of Primates , 2016, GCPR.

[35]  Raymond A. Webster,et al.  Modeling misidentification errors in capture-recapture studies using photographic identification of evolving marks. , 2009, Ecology.

[36]  Joseph J. Paton,et al.  Big behavioral data: psychology, ethology and the foundations of neuroscience , 2014, Nature Neuroscience.

[37]  Peter M. Kappeler,et al.  Long-Term Field Studies of Primates , 2012, Springer Berlin Heidelberg.

[38]  Václav Snásel,et al.  Muzzle-Based Cattle Identification Using Speed up Robust Feature Approach , 2015, 2015 International Conference on Intelligent Networking and Collaborative Systems.

[39]  Anil K. Jain,et al.  Biometric Systems: Technology, Design and Performance Evaluation , 2004 .

[40]  Horst Bischof,et al.  Stereoscopic motion analysis in densely packed clusters: 3D analysis of the shimmering behaviour in Giant honey bees , 2011, Frontiers in Zoology.

[41]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[42]  Luigi Cinque,et al.  Identifying elephant photos by multi-curve matching , 2008, Pattern Recognit..

[43]  MARIA VLAD,et al.  A Survey of Livestock Identification Systems , 2012 .

[44]  Artur Gmerek,et al.  Automatic identification of bird species: A comparison between kNN and SOM classifiers , 2012, 2012 Joint Conference New Trends In Audio & Video And Signal Processing: Algorithms, Architectures, Arrangements And Applications (NTAV/SPA).

[45]  Alfred Lameli,et al.  Correction: The Effect of Perceived Regional Accents on Individual Economic Behavior: A Lab Experiment on Linguistic Performance, Cognitive Ratings and Economic Decisions , 2015, PloS one.

[46]  T. Burghardt,et al.  Real-time Face Detection and Tracking of Animals , 2006, 2006 8th Seminar on Neural Network Applications in Electrical Engineering.

[47]  Sanjay Kumar Singh,et al.  Poster: A Real-time Cattle Recognition System using Wireless Multimedia Networks , 2016, MobiSys '16 Companion.

[48]  Kofi Appiah,et al.  Automatic classification of flying bird species using computer vision techniques , 2016, Pattern Recognit. Lett..

[49]  Jenq-Neng Hwang,et al.  A Feature Learning and Object Recognition Framework for Underwater Fish Images , 2016, IEEE Transactions on Image Processing.

[50]  Robinson Piramuthu,et al.  Mine the fine: Fine-grained fragment discovery , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[51]  J. Holmberg,et al.  Estimating population size, structure, and residency time for whale sharks Rhincodon typus through collaborative photo-identification , 2009 .

[52]  T. Clutton‐Brock,et al.  Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. , 2010, Trends in ecology & evolution.

[53]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[54]  C. Bradshaw,et al.  Spot the match – wildlife photo-identification using information theory , 2007, Frontiers in Zoology.

[55]  Adnan Amin,et al.  Fingerprint classification: a review , 2004, Pattern Analysis and Applications.

[56]  Adriano Bressane,et al.  Pattern recognition in trunk images based on co-occurrence descriptors: A proposal applied to tree species identification , 2015, 2015 Latin America Congress on Computational Intelligence (LA-CCI).

[57]  Yvon Le Maho,et al.  Reliability of flipper-banded penguins as indicators of climate change , 2011, Nature.

[58]  Jaclyn A. Smolinsky,et al.  Assessment of Bird Response to the Migratory Bird Habitat Initiative Using Weather-Surveillance Radar , 2014 .

[59]  Aboul Ella Hassanien,et al.  A robust cattle identification scheme using muzzle print images , 2013, 2013 Federated Conference on Computer Science and Information Systems.

[60]  Andreas Ernst,et al.  Fast face detection and species classification of African great apes , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[61]  Hanspeter Pfister,et al.  Trainable Convolution Filters and Their Application to Face Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[62]  Francis Butler,et al.  Using Muzzle Pattern Recognition as a Biometric Approach for Cattle Identification , 2007 .

[63]  Sanjay Kumar Singh,et al.  Feature Selection and Recognition of Face by using Hybrid Chaotic PSO-BFO and Appearance-Based Recognition Algorithms , 2015, Int. J. Nat. Comput. Res..

[64]  Thomas Serre,et al.  Automated home-cage behavioural phenotyping of mice. , 2010, Nature communications.

[65]  Sanjay Kumar Singh,et al.  Biometric Recognition for Pet Animal , 2014 .

[66]  Bin Fan,et al.  Local Intensity Order Pattern for feature description , 2011, 2011 International Conference on Computer Vision.

[67]  D D Wardrope,et al.  Problems with the use of ear tags in cattle , 1995, Veterinary Record.

[68]  Heidi Dewar,et al.  Vulnerabilities and fisheries impacts: The uncertain future of manta and devil rays , 2016 .

[69]  Gérard Chollet,et al.  A reference system for animal biometrics: Application to the northern leopard frog , 2014, 2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP).

[70]  Thomas G. Dietterich,et al.  Automated insect identification through concatenated histograms of local appearance features: feature vector generation and region detection for deformable objects , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[71]  J. Andrew Royle,et al.  Hierarchical Modeling and Inference in Ecology: The Analysis of Data from Populations, Metapopulations and Communities , 2008 .

[72]  Min-Chun Hu,et al.  Locality Constrained Sparse Representation for Cat Recognition , 2016, MMM.

[73]  A. M. Johnston,et al.  Welfare implications of identification of cattle by ear tags , 1996, Veterinary Record.

[74]  C. Town,et al.  Manta Matcher: automated photographic identification of manta rays using keypoint features , 2013, Ecology and evolution.

[75]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[76]  Luca Mainetti,et al.  A tracking system for laboratory mice to support medical researchers in behavioral analysis , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[77]  Robert B. Fisher,et al.  Detecting abnormal fish trajectories using clustered and labeled data , 2013, 2013 IEEE International Conference on Image Processing.

[78]  Thomas Stieglitz,et al.  RFID Technology for Continuous Monitoring of Physiological Signals in Small Animals , 2015, IEEE Transactions on Biomedical Engineering.

[79]  E. V. Chelysheva.  A New Approach to Cheetah Identification , .

[80]  M. O'Neill,et al.  Automated species identification: why not? , 2004, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[81]  Jeffrey J. Buler,et al.  Geographic position and landscape composition explain regional patterns of migrating landbird distributions during spring stopover along the northern coast of the Gulf of Mexico , 2016, Landscape Ecology.

[82]  Liang Wang,et al.  Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition , 2007, IEEE Transactions on Image Processing.

[83]  Li-Chin Tsai,et al.  Species identification of rhinoceros horns using the cytochrome b gene. , 2003, Forensic science international.

[84]  Nan Jiang,et al.  Automatic video-based analysis of animal behaviors , 2010, 2010 IEEE International Conference on Image Processing.

[85]  Christophe Doignon,et al.  Visual tracking of small animals based on real-time Level Set Method with fast infra-red thermographic imaging , 2011, 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE).

[86]  Dezhen Song,et al.  System and algorithms for an autonomous observatory assisting the search for the Ivory-Billed Woodpecker , 2008, 2008 IEEE International Conference on Automation Science and Engineering.

[87]  Takeshi Saitoh,et al.  Image-based fish recognition , 2015, 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[88]  Chelsea Finn,et al.  Sloop: A pattern retrieval engine for individual animal identification , 2015, Pattern Recognit..

[89]  Anil K. Jain.  Biometric recognition: how do I know who you are? , 2004, Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, 2004..

[90]  K. Martin-Smith,et al.  Photo-identification of individual weedy seadragons Phyllopteryx taeniolatus and its application in estimating population dynamics. , 2011, Journal of fish biology.

[91]  Hong L. Choi,et al.  The identification of Japanese black cattle by their faces , 2005 .

[92]  Sneha Jain,et al.  Analysis and design of neural network based image retrieval system for identification of species , 2016, 2016 International Conference on Computation of Power, Energy Information and Commuincation (ICCPEIC).

[93]  Deborah Estrin,et al.  Heartbeat of a nest: Using imagers as biological sensors , 2010, TOSN.

[94]  Chelsea Finn,et al.  Relevance Feedback in Biometric Retrieval of Animal Photographs , 2014, MCPR.

[95]  Xiaolin Chen,et al.  Insect species recognition using discriminative local soft coding , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[96]  S. T. Buckland,et al.  Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. , 2010, Trends in ecology & evolution.

[97]  J. Ahumada,et al.  Community structure and diversity of tropical forest mammals: data from a global camera trap network , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[98]  ARY NOVIYANTO.  Automatic Cattle Identification based on Muzzle Photo Using Speed-Up Robust Features Approach , 2012 .

[99]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[100]  S. Omholt,et al.  Phenomics: the next challenge , 2010, Nature Reviews Genetics.

[101]  Santosh Kumar,et al.  Face Recognition of Cattle: Can it be Done? , 2016, Proceedings of the National Academy of Sciences, India Section A: Physical Sciences.

[102]  Jorge E. Camargo,et al.  Visualization of audio records for automatic bird species identification , 2015, 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA).

[103]  Alfred Lameli,et al.  The Effect of Perceived Regional Accents on Individual Economic Behavior: A Lab Experiment on Linguistic Performance, Cognitive Ratings and Economic Decisions , 2015, PloS one.

[104]  Karmele López de Ipiña,et al.  First approach to the analysis of spontaneous activity of mice based on Permutation Entropy , 2015, 2015 4th International Work Conference on Bioinspired Intelligence (IWOBI).

[105]  Thomas G. Dietterich.  Machine Learning in Ecosystem Informatics and Sustainability , 2009, IJCAI.

[106]  Karol J. Piczak.  Recognizing Bird Species in Audio Recordings using Deep Convolutional Neural Networks , 2016, CLEF.

[107]  Alexios Glaropoulos,et al.  A computer-vision system and methodology for the analysis of fish behavior , 2012 .

[108]  Aniati Murni Arymurthy,et al.  Beef cattle identification based on muzzle pattern using a matching refinement technique in the SIFT method , 2013 .

[109]  Christian Dietz,et al.  A continental-scale tool for acoustic identification of European bats , 2012 .

[110]  Sreela Sasi,et al.  Recognition of stonefish from underwater video , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[111]  Rahman Tashakkori,et al.  Image processing for honey bee hive health monitoring , 2015, SoutheastCon 2015.

[112]  Antonio Romano,et al.  APHIS: A new software for photo-matching in ecological studies , 2015, Ecol. Informatics.

[113]  Jenq-Neng Hwang,et al.  Supervised and Unsupervised Feature Extraction Methods for Underwater Fish Species Recognition , 2014, 2014 ICPR Workshop on Computer Vision for Analysis of Underwater Imagery.

[114]  Mei Fangquan,et al.  Identification of beef cattle by analyzing images of their muzzle patterns lifted on paper. , 2002 .

[115]  Tilo Burghardt,et al.  Generic Phase Curl Localisation for an Individual Identification of Turing-patterned Animals , 2010 .

[116]  Hany Farid,et al.  A computer‐assisted system for photographic mark–recapture analysis , 2012 .

[117]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[118]  Hamdi A. Mahmoud,et al.  Automatic cattle muzzle print classification system using multiclass support vector machine , 2015 .

[119]  F. Galimberti,et al.  Using an unmanned aerial vehicle for environmental research and monitoring at Sea Lion Island Project report , 2017 .

[120]  David W. Jacobs,et al.  Dog Breed Classification Using Part Localization , 2012, ECCV.

[121]  K. U. Karanth,et al.  A tiger cannot change its stripes: using a three-dimensional model to match images of living tigers and tiger skins , 2009, Biology Letters.

[122]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[123]  Stephen T. Buckland,et al.  Aerial surveys of seabirds: the advent of digital methods , 2012 .

[124]  Takeshi Naemura,et al.  Detection of small birds in large images by combining a deep detector with semantic segmentation , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[125]  Mubarak Shah,et al.  Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[126]  Andrew Calway,et al.  Tracking Many Objects Using Subordinated Condensation , 2002, BMVC.

[127]  Shi Chen,et al.  Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals? , 2014, PloS one.

[128]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[129]  Kofi Appiah,et al.  A computer vision approach to classification of birds in flight from video sequences , 2015 .

[130]  Marcella J. Kelly,et al.  COMPUTER-AIDED PHOTOGRAPH MATCHING IN STUDIES USING INDIVIDUAL IDENTIFICATION: AN EXAMPLE FROM SERENGETI CHEETAHS , 2001 .

[131]  Tyler R. Bonnell,et al.  Sparse movement data can reveal social influences on individual travel decisions , 2015 .

[132]  A. Baranov,et al.  Breed differences and intra-breed genetic variability of dermatoglyphic pattern of cattle. , 1993, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[133]  James D. Nichols,et al.  Estimating survival in photographic capture–recapture studies: overcoming misidentification error , 2011 .

[134]  Weiwei Zhang,et al.  Cat Head Detection - How to Effectively Exploit Shape and Texture Features , 2008, ECCV.

[135]  Xiaojiang Chen,et al.  The application of the Internet of Things to animal ecology. , 2015, Integrative zoology.

[136]  Kate E. Jones,et al.  PanTHERIA: a species‐level database of life history, ecology, and geography of extant and recently extinct mammals , 2009 .

[137]  Stefanos Zafeiriou,et al.  Robust Statistical Frontalization of Human and Animal Faces , 2016, International Journal of Computer Vision.

[138]  Terry C. Greene,et al.  South Polar Skua breeding populations in the Ross Sea assessed from demonstrated relationship with Adélie Penguin numbers , 2017, Polar Biology.

[139]  Suriayati Chuprat,et al.  Mouse movement behavioral biometric systems , 2011, 2011 International Conference on User Science and Engineering (i-USEr ).

[140]  Michael B. Bennett,et al.  Morphological measurements of manta rays (Manta birostris) with a description of a foetus from the east coast of Southern Africa , 2008 .

[141]  S. Santhosh Baboo,et al.  Identification of Olive Ridley Turtle Using Feature Extraction , 2014, 2014 International Conference on Intelligent Computing Applications.

[142]  Ahmed Bouridane,et al.  Cascaded multimodal biometric recognition framework , 2014, IET Biom..

[143]  Neill W. Campbell,et al.  Identifying quadruped gait in wildlife video , 2005, IEEE International Conference on Image Processing 2005.

[144]  Thomas M. Butynski,et al.  Comparative Ecology of Blue Monkeys (Cercopithecus Mitis) in High‐ and Low‐Density Subpopulations , 1990 .

[145]  Haroon Idrees,et al.  Detection and Tracking of Large Number of Targets in Wide Area Surveillance , 2010, ECCV.

[146]  J. Petherick,et al.  Animal welfare issues associated with extensive livestock production: The northern Australian beef cattle industry , 2005 .

[147]  Dezhen Song,et al.  Automatic Bird Species Filtering Using a Multimodel Approach , 2015, IEEE Transactions on Automation Science and Engineering.

[148]  Jinzhong Yang,et al.  The thin plate spline robust point matching (TPS-RPM) algorithm: A revisit , 2011, Pattern Recognit. Lett..

[149]  Farzin Deravi,et al.  Automatic identification of wildlife using Local Binary Patterns , 2012 .

[150]  Matthew R. Schofield,et al.  Incorporating Genotype Uncertainty into Mark–Recapture‐Type Models For Estimating Abundance Using DNA Samples , 2009, Biometrics.

[151]  Sanjay Kumar Singh,et al.  Feature Selection and Recognition of Muzzle Point Image Pattern of Cattle by Using Hybrid Chaos BFO and PSO Algorithms , 2016, Advances in Chaos Theory and Intelligent Control.

[152]  Sanjay Kumar Singh,et al.  A Fast Cattle Recognition System using Smart devices , 2016, ACM Multimedia.

[153]  Tanya Y. Berger-Wolf,et al.  Biometric animal databases from field photographs: identification of individual zebra in the wild , 2011, ICMR.

[154]  David A. Forsyth,et al.  Building models of animals from video , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[155]  Thomas G. Dietterich,et al.  Automated Insect Identification through Concatenated Histograms of Local Appearance Features , 2007, WACV.

[156]  Boris Bratnina.  AKADEMSKO PISANJE U DRUŠTVENIM NAUKAMA , 2011 .

[157]  James B. Grand,et al.  Variation in Detection Among Passive Infrared Triggered-cameras Used in Wildlife Research , 2010 .

[158]  Fernando González-Ladrón-de-Guevara,et al.  Crowdsourcing Fundamentals: Definition and Typology , 2015 .

[159]  Ilse van Opzeeland,et al.  Management and research applications of real-time and archival passive acoustic sensors over varying temporal and spatial scales , 2009 .

[160]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[161]  A. Loos,et al.  Towards automated visual identification of primates using face recognition , 2012, 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP).

[162]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[163]  I. Cuthill,et al.  Spotting the difference: towards fully-automated population monitoring of African penguins Spheniscus demersus , 2010 .

[164]  H. Kühl,et al.  Animal biometrics: quantifying and detecting phenotypic appearance. , 2013, Trends in ecology & evolution.

[165]  Pietro Perona,et al.  Automated image-based tracking and its application in ecology. , 2014, Trends in ecology & evolution.

[166]  Steve Kelling,et al.  Data-intensive science applied to broad-scale citizen science. , 2012, Trends in ecology & evolution.

[167]  Matti Pietikäinen,et al.  Differentiating spontaneous from posed facial expressions within a generic facial expression recognition framework , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[168]  M. Vences,et al.  Evidence for recent gene flow between north-eastern and south-eastern Madagascan poison frogs from a phylogeography of the Mantella cowani group , 2007, Frontiers in Zoology.

[169]  Xiaojun Qi,et al.  Face recognition under illumination variations based on eight local directional patterns , 2015, IET Biom..

[170]  Weiwei Zhang,et al.  From Tiger to Panda: Animal Head Detection , 2011, IEEE Transactions on Image Processing.

[171]  Björn Krüger,et al.  Retrieval, recognition and reconstruction of quadruped motions , 2014, 2014 International Conference on Computer Graphics Theory and Applications (GRAPP).

[172]  Lian Li,et al.  Identification of fish species based on image processing and statistical analysis research , 2014, 2014 IEEE International Conference on Mechatronics and Automation.

[173]  Stefano Zapperi,et al.  Role of the Number of Microtubules in Chromosome Segregation during Cell Division , 2015, PloS one.

[174]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[175]  Andrew Y. Ng,et al.  Emergence of Object-Selective Features in Unsupervised Feature Learning , 2012, NIPS.