Over　the　past　few　decades,　hand-based　multimodal　biometrics　systems　have　achieved　significant　attention　because　of　their　high　security,　accuracy,　and　anti-counterfeiting.　Various　hand　physiological　biometric　modalities　have　been　explored　for　identity　authentication,　i.e.,　fingerprint,　finger　knuckle　print,　palmprint,　palm　vein,　and　dorsal　hand　vein　traits.　This　study　provides　a　comprehensive　review　focusing　on　the　interface　of　different　hand　biometric　traits　and　presents　an　overview　of　hand-based　multimodal　biometrics　methods.　The　framework　of　this　paper　is　divided　into　three　main　categories.　Firstly,　we　introduce　the　characteristics　of　four　levels　of　hand-based　biometrics　in　detail.　Following　this,　several　typical　image　capturing　devices　and　image　preprocessing　techniques　of　various　hand-based　biometrics　are　reviewed.　Moreover,　existing　publicly　available　and　widely　used　hand-based　multimodal　biometrics　databases　are　then　summarized.　Subsequently,　the　hand-based　multimodal　biometrics　methods　are　categorized　into　sensor-level　fusion,　feature-level　fusion,　score-level　fusion,　rank-level　fusion,　and　decision-level　fusion.　Additionally,　the　recent　hybrid　fusion-based　and　deep　learning-based　hand　multimodal　biometrics　approaches　are　analyzed　and　discussed.　Furthermore,　we　conduct　a　performance　analysis　of　the　abovementioned　algorithms　from　the　recent　literature.　At　last,　challenges,　trends,　and　some　recommendations　related　to　hand-based　multimodal　biometrics　are　drawn　to　give　some　research　directions.　©　2024　Elsevier　B.V.